Dissertations / Theses: 'Fuzzy Measures'

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Santos, H?lida Salles. "A new class of fuzzy subsethood measures." PROGRAMA DE P?S-GRADUA??O EM SISTEMAS E COMPUTA??O, 2016. https://repositorio.ufrn.br/jspui/handle/123456789/23644.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
Este trabalho tem o objetivo de introduzir uma nova classe de medidas de inclus?o difusa entre conjuntos difusos. Esta nova abordagem foi baseada nas axiomatiza??es mais conhecidas com a vantagem de utilizar o m?todo de constru??o de tais medidas agregando os operadores de implica??o. Esses operadores satisfazem algumas propriedades que t?m sido amplamente investigadas na literatura, de forma que, por exemplo, a medida de inclus?o proposta por Goguen torna-se um caso particular da nossa proposta de medida de inclus?o. Apresentamos tamb?m diferentes m?todos de constru??o utilizando automorfismos e provamos que com tais medidas podemos construir n?o s? medidas de entropia, mas tamb?m dist?ncias, fun??es p?nalti e medidas de similaridade entre conjuntos difusos.
The idea of inclusion for fuzzy sets was firstly introduced by L. Zadeh in 1965 and since then many other studies proposed alternatives to indicate a degree to which a fuzzy set is included into another fuzzy set, called an inclusion degree or a subsethood measure. In this work we present a new class of fuzzy subsethood measures between fuzzy sets. We introduce a new definition of a fuzzy subsethood measure as an intersection of other axiomatizations by aggregating fuzzy implication operators. We also provide some construction methods to obtain these fuzzy subsethood measures. With our approach we recover some of the classical measures which have been discussed in the literature, as the one given by Goguen. We also show how we can use our developments to generate fuzzy entropies, fuzzy distances, penalty functions and similarity measures. Finally we study some fuzzy indexes generated from this new class of fuzzy subsethood measures.

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Parlikar, Virendra R. "Fuzzy non-radial measures of relative technical efficiency using DEA." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-11182008-063112/.

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Bashon, Yasmina M. "Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
Libyan Embassy

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Bashon, Yasmina Massoud. "Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.

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IONESCU, MIRCEA MARIAN. "ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.

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Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.

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FarinWata, Shehu Saíd. "Performance assessment of fuzzy logic control systems via stability and robustness measures." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/14797.

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Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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9

Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16456/1/Xiaofeng_Liu_Thesis.pdf.

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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.

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Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16456/.

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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.

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11

Hamrawi, Hussam. "Type-2 fuzzy alpha-cuts." Thesis, De Montfort University, 2011. http://hdl.handle.net/2086/5137.

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Systems that utilise type-2 fuzzy sets to handle uncertainty have not been implemented in real world applications unlike the astonishing number of applications involving standard fuzzy sets. The main reason behind this is the complex mathematical nature of type-2 fuzzy sets which is the source of two major problems. On one hand, it is difficult to mathematically manipulate type-2 fuzzy sets, and on the other, the computational cost of processing and performing operations using these sets is very high. Most of the current research carried out on type-2 fuzzy logic concentrates on finding mathematical means to overcome these obstacles. One way of accomplishing the first task is to develop a meaningful mathematical representation of type-2 fuzzy sets that allows functions and operations to be extended from well known mathematical forms to type-2 fuzzy sets. To this end, this thesis presents a novel alpha-cut representation theorem to be this meaningful mathematical representation. It is the decomposition of a type-2 fuzzy set in to a number of classical sets. The alpha-cut representation theorem is the main contribution of this thesis. This dissertation also presents a methodology to allow functions and operations to be extended directly from classical sets to type-2 fuzzy sets. A novel alpha-cut extension principle is presented in this thesis and used to define uncertainty measures and arithmetic operations for type-2 fuzzy sets. Throughout this investigation, a plethora of concepts and definitions have been developed for the first time in order to make the manipulation of type-2 fuzzy sets a simple and straight forward task. Worked examples are used to demonstrate the usefulness of these theorems and methods. Finally, the crisp alpha-cuts of this fundamental decomposition theorem are by definition independent of each other. This dissertation shows that operations on type-2 fuzzy sets using the alpha-cut extension principle can be processed in parallel. This feature is found to be extremely powerful, especially if performing computation on the massively parallel graphical processing units. This thesis explores this capability and shows through different experiments the achievement of significant reduction in processing time.

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Marija, Paunović. "Mere neodređenosti i primena u aktuarstvu." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=110708&source=NDLTD&language=en.

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Predmet istraživanja ovog rada su mere neodređenosti, posebno merakredibliteta, kao i mogućnost njihove primene u aktuarstvu. U ciljugeneralizacije teorije kredibiliteta, uvedena je nova mera, nazvana mera c-kredibiliteta. Mera c-kredibiliteta na X je skupovna funkcija takva da suzadovoljene osobine normalnosti, monotonosti, samodualnosti i maksimalnosti.Za nju su dokazane neke osobine kao što su npr. subaditivnost ipoluneprekidnost. Nadalje, definisan je integral zasnovan na meri c-kredibiliteta, a navedena su i dokazana određena svojstva. Nova mera je uvedenai u fazi okruženju kao agregirana vrednost mera mogućnosti i neophodnosti.
This thesis studies uncertainty measures, especially credibility measure, as well asthe possibility of their application in actuaries. In order to generalize credibility theory,a new fuzzy measure is proposed, called c − credibility measure. C − credibilitymeasure on X is a set function that satisfies normality, monotonicity, self-duality andmaximality. Certain properties of the c−credibility measure are proved, such as, forexample, subadditivity and semicontinuity. Furthermore, an integral based on thismeasure is defined, in analogy to the existing integrals, and its properties are proved.Then, the credibility measure in a fuzzy environment is introduced as the aggregatevalue of the possibility and necessity measures.

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Onescu, Mircea Marian. "Adaptive measures of similarity - fuzzy hamming distance - and its applications to pattern recognition problems." Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1163708478.

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Thesis (Ph. D.)--University of Cincinnati, 2006..
Title from electronic thesis title page (viewed Jan.27, 2007). Includes abstract. Keywords: Fuzzy Hamming Distance, artificial intelligence, fuzzy, image retrieval system Includes bibliographical references.

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Bas, Bohdan, and Богдан Валерійович Бас. "Aircraft incident and accident investigation techniques with the help of proactive measures." Thesis, Національний авіаційний університет, 2020. https://er.nau.edu.ua/handle/NAU/45661.

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Робота публікується згідно наказу ректора від 21.01.2020 р. №008/од "Про перевірку кваліфікаційних робіт на академічний плагіат 2019-2020р.р. навчальному році ". Керівник проекту: д.т.н., проф. Харченко Володимир Петрович
Civil aviation is a strategic priority of geopolitical, social and economic development of Ukraine and an important part of production and social infrastructure. Its sustainable, efficient operation is a necessary condition for national security, sustainable economic growth and improving living standards. With the beginning of the restructuring of economic relations, the volume of aviation activity in Ukraine has decreased significantly. The financial situation of aviation enterprises has become more complicated. It has led to a reduction in the development and improvement of civil aviation, and led not only to a slowdown in scientific and technological progress, but also to a deterioration in its technical condition. The fashion for the creation of "independent structural units" within enterprises and the industry as a whole in search of economic benefit has pushed to the background the issue of flight safety. Annual, long-term structural reorganizations with the Aviation Administration of Ukraine do not allow effective and efficient management. The predominant interests of "commerce" lead to the widespread use of strictly prohibited methods: the irreversible process of deconstruction aircraft and rearranging units, engines and equipment from one aircraft to another, extending the resources of aircraft without a proper assessment of its condition which inevitably leads to complete lack of control on the part of the Aviation Administration of Ukraine. It indicates that the level of flight safety in the air navigation system of Ukraine is not provided. A number of problems in safety theory are caused by imperfect methods of scientific research, in particular, when planning airspace.
Цивільна авіація є стратегічним пріоритетом геополітичного, соціального та економічного розвитку України та важливою частиною виробничої та соціальної інфраструктури. Його стійке, ефективне функціонування є необхідною умовою національної безпеки, стійкого економічного зростання та підвищення рівня життя. З початком перебудови економічних відносин обсяг авіаційної діяльності в Україні значно зменшився. Фінансовий стан авіаційних підприємств ускладнився. Це призвело до зменшення розвитку та вдосконалення цивільної авіації та призвело не лише до уповільнення науково-технічного прогресу, але й до погіршення її технічного стану. Мода на створення "самостійних структурних підрозділів" на підприємствах та в цілому в галузі в пошуках економічної вигоди відсунула на другий план питання безпеки польотів. Щорічні, довгострокові структурні реорганізації з Авіаційною адміністрацією України не дозволяють ефективно і результативно керувати. Переважаючі інтереси "торгівлі" призводять до широкого використання суворо заборонених методів: незворотний процес деконструкції літаків та перестановка агрегатів, двигунів та обладнання з одного літака на інший, розширення ресурсів літака без належної оцінки його стану, що неминуче призводить до повної відсутності контролю з боку авіаційного управління України. Це свідчить про те, що рівень безпеки польотів в аеронавігаційній системі України не забезпечується. Ряд проблем в теорії безпеки спричинений недосконалими методами наукових досліджень, зокрема, при плануванні повітряного простору.

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Roberts, Timothy. "Design of Objective Quality Measures for Time-Scale Modification of Audio." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/401637.

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Time-Scale Modification (TSM) is a well-researched field and allows for time-domain manipulation of a signal without modifying the pitch or timbre. Many TSM methods have been presented, however quantitative results on the quality of these methods are rare, with most methods reporting informal listening tests. This is likely due to the timecommitment and cost of subjective testing. Additionally, an objective measure of quality has not yet been developed that is suitable for timescaled signals. This dissertation describes the design of e ective objective measures of quality for TSM. TSM methods are, generally, single channel algorithms that give poor results when applied to multi-channel signals, as the phase relationship between channels must be maintained. This dissertation proposes a method and additional variant for maintaining the phase relationship between channels and retaining the presence in the centre of the stereo signal. The method involves pre- and post-processing the signal, with the variant processing each frame for real-time suitability. Sum and di erence transformations of the stereo signal are used for TSM and result in a large improvement in stereo phase coherence, consequently maintaining the stereo field. The proposed method produces a highquality stereo output and greatly improves quality over the independent channel processing method. It also allows for simple implementation around all existing TSM frameworks. A modification to the Epoch-Synchronous Overlap-Add (ESOLA) TSM algorithm is proposed in this dissertation. The proposed method, Fuzzy Epoch-Synchronous Overlap-Add, improves on the previous ESOLA method through cross-correlation of time-smeared epochs before overlap-adding. This reduces distortion and artefacts while the speaker's fundamental frequency is stable, as well as reducing artefacts during pitch modulation. The proposed method is tested against well-known TSM algorithms. It is preferred over ESOLA and gives similar performance to other TSM algorithms for voice signals. It is also shown that this algorithm can work effectively with solo instrument signals containing strong fundamental frequencies. No effective objective measure of quality for TSM exists. This dissertation details the creation, subjective evaluation and analysis of a dataset, for use in the development of an objective measure of quality for TSM. Comprising two parts, the training subset contains 88 source files processed using six TSM methods at 10 time-scales, while the testing subset contains 20 source files processed using three additional methods at four time-scales. The source material contains speech, solo harmonic and percussive instruments, sound effects and a range of music genres. 42,529 ratings were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; equivalence between expert and non-expert listeners; negligible di erences between participants with and without hearing issues; and negligible di erences between testing modalities. Comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average loss and correlation values of subjective sessions. An objective measure of quality for time-scaled audio is proposed that makes use of the previously developed dataset and improves on reported results. The measure uses hand-crafted features and a fully connected network to predict subjective mean opinion scores. Basic and Advanced Perceptual Evaluation of Audio Quality features are used in addition to nine features specific to TSM artefacts. Six methods of alignment are explored, with interpolation of the reference magnitude spectrum to the length of the test magnitude spectrum giving the best performance. The proposed measure achieves an average Root Mean Squared Error (RMSE) of 0.490 and a mean Pearson Correlation Coe cient (PCC) of 0.864, equivalent to 97th and 82nd percentiles of subjective sessions respectively. The proposed measure is used to evaluate TSM algorithms, finding that Elastique gives the highest objective quality for solo instrument and voice signals, while the Identity Phase-Locking Phase Vocoder gives the highest objective quality for music signals and the best overall quality. Two single-ended objective quality measures for time-scaled audio are also proposed. These measure do not require a reference signal, nor alignment. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network, and are fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average RMSE of 0.608 and 0.576, and a mean PCC of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. A literature review is included with required background knowledge. It includes the fundamentals of sound perception, sound capture, digital signal processing, time-scale modification methods used within research, and subjective and objective measures of quality. Full implementation of all proposed methods and measures can be found at github.com/zygurt/TSM, while the labelled dataset is available at http://ieee-dataport.org/1987.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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16

Abu-halaweh, Nael Mohammed. "Integrating Information Theory Measures and a Novel Rule-Set-Reduction Tech-nique to Improve Fuzzy Decision Tree Induction Algorithms." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/cs_diss/48.

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Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn.

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Dawood, Ghalieb. "Applying fuzzy-set theoretic poverty measures within a developmental local government context : the Khayelitsha - Mitchell's Plain case study." Thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/6759.

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Includes bibliographical references (leaves 100-108).
This paper attempts to demonstrate the importance of the linkage between the presence of poverty and the nature of governance, something largely omitted from poverty studies in South Africa. The context of this investigation was the establishment of the new local government model (i.e. Developmental Local Government), which puts governance at the forefront of addressing poverty effectively. The new governance model adopts a multidimensional poverty paradigm in its Integrated Development Planning (IDP). However, in this study we have examined whether the approach adopted (i.e. Basic Needs) is necessarily the best multidimensional approach available. We have given preference to the capabilities approach with its emphasis on well-being where people are the beneficiaries of development rather than the basic needs approach where the emphasis is on goods and services as a means to good life. Sen's Capabilities Approach was operationalised by adopting a relatively new methodology (Le. fuzzy-set theoretic poverty measures) for measuring multidimensional poverty in the Khayelitsha Mitchell's Plain (KMP) magisterial district using the Census 2001 dataset. Our results show that unemployment, housing and low incomes need the most attention in KMP. Furthermore, the fuzzy-set measures, which view poverty as opaque and vague, yield more detailed policy information, thus preventing the single-policy response dominating many IDPs at present. As a medium term policy response, it is suggested that the implementation of the extended public works programme in KMP has the potential to significantly address both the material and non-material capability failure existing in KMP.

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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, and O. Skrynnyk. "Neuro-Genetic Hybrid System for Management of Organizational Development Measures." Thesis, RWTH Aachen University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/85490.

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Current practical experience in measuring the effectiveness of organizational development activities is largely based on the evaluation of surveys. In this paper we present an approach based on an artificial neural network with elements of a fuzzy approach and a genetic algorithm to control organizational development. Based on genetic algorithms, the organizational development measures are initiated, selected, combined or mutated with the goal of finding the best possible solution for each concrete case. Since many variables have the uncertain set of their values, the use of a hybrid neuro-fuzzy mechanism makes it possible to analyze the behavioral components up to the combinations of needs and thereby select the appropriate organizational development measures. The system is designed to ensure the long-term effectiveness of organizational development measures. We supplement the previously known measures of organizational development with technology-based in order to increase the degree of automation in practice. This article is intended as an orientation for other scientists who are researching the same topic and are interested in the current state of the art, as well as for companies who want to ensure compliance with internal company rules using digital tools.

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Costa, Antonio José Leal. "Mensuração do grau de incapacitação funcional através de um modelo baseado na lógica fuzzy." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-30072014-143639/.

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Objetivo. Este estudo diz respeito à mensuração da incapacitação funcional, e seu uso no cálculo de indicadores de saúde e qualidade de vida. O seu objetivo foi estimar o grau de incapacitação associado a diferentes níveis funcionais, através de um modelo baseado na lógica fuzzy. Métodos. Desenvolveu-se um modelo lingüístico fuzzy, baseado na opinião de especialistas. As variáveis de entrada do modelo foram, respectivamente, a mobilidade, a atividade física e a atividade social. Ao todo, foram definidas 100 regras fuzzy relacionando as variáveis de entrada, a partir das quais determinou-se o grau de incapacidade funcional. As avaliações dos diferentes funcionais foram feitas sobre uma escala de avaliação fuzzy. Para cada nível funcional, o modelo gera uma estimativa do grau de incapacitação, d, no intervalo entre 0 e 10, valores estes representantes da morte e do melhor estado de saúde imaginável, respectivamente. Resultados. Ao todo, foram avaliados 100 diferentes níveis funcionais. A título de exemplificação, para o nível funcional caracterizado por um adulto que trabalha, mas apresenta restrições para desenvolver outras atividades sociais, necessita auxílio para usar transporte público ou dirigir, e anda com limitações, a estimativa de d foi igual a 5,8, segundo um dos especialistas. Isto significa que um ano nesta condição equivale a 0,58 anos de vida com saúde, sem qualquer tipo de limitação funcional. Conclusões. O modelo fuzzy foi considerado uma alternativa consistente para a mensuração do grau de incapacitação. Ele emula o raciocínio humano, e provê uma estrutura adequada para lidar com incertezas e imprecisões, características inerentes ao processo de mensuração da incapacitação funcional.
Objective. This study is concerned with the measurement of disability, and its use in health and quality of life indicators. The aim was to estimate the degree of disability associated with varying functional levels, through a model based on fuzzy logic. Methods. A fuzzy linguistic model was developed, based on experts opinion. Three fuzzy input variables were considered according to: social activity, mobility and physical activity. A set with 100 fuzzy rules was derived and considered as consequent for each rule the functional disability. Functional levels were evaluated separately for each input variables on a fuzzy rating scale. The model provides a numerical estimate, d, of an individual\'s functional disability state, ranging from 0 to 10, which represent death and optimal function, respectively, obtained through defuzzification. Results. A total of 100 multidimensional functional levels were evaluated. As an example, for a functional level characterised by an individual who works but performs restrictively other social activities, needs help to use public transport and walks with physical limitations, d was estimated as 5,8, according to one of the experts. This means that one year in such a state is equivalent to 0.58 years of well life, in the absence of any kind of functional disability. Conclusions. The fuzzy model was considered a consistent alternative for the estimation of the degree of disability. It emulates human thinking, and provides an adequate framework to deal with uncertainty and imprecision, which are inherent in the measurement of functional disability.

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Goyal, Vivek. "A Recommendation System Based on Multiple Databases." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

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Judd, John David. "Stream splitting in support of intrusion detection." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FJudd.pdf.

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Taghizadeh, Vahed Amir. "Fan And Pitch Angle Selection For Efficient Mine Ventilation Using Analytical Hierachy Process And Neuro Fuzzy Approach." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614320/index.pdf.

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Ventilation is a critical task in underground mining operation. Lack of a good ventilation system causes accumulation of harmful gases, explosions, and even fatalities. A proper ventilation system provides adequate fresh air to miners for a safe and comfortable working environment. Fans, which provide air flow to different faces of a mine, have great impact in ventilation systems. Thus, selection of appropriate fans for a mine is the acute task. Unsuitable selection of a fan decreases safety and production rate, which increases capital and operational costs. Moreover, pitch angle of fans&rsquo
blades plays an important role in fan&rsquo
s efficiency. Therefore, selection of a fan and its pitch angle, which yields the maximum efficiency, is an emerging issue for an efficient mine ventilation. The main objective of this research study is to provide a decision making methodology for the selection of a main fan and its appropriate pitch angle for efficient mine ventilation. Nowadays, analytical hierarchy process as multi criteria decision making is used, and it yields outputs based on pairwise comparison. On the other hand, Fuzzy Logic as a soft computing method was combined with analytical hierarchy process and combined model did not yield appropriate results
because Fuzzy AHP increased uncertainty ratio in this study. However, fuzzy analytical hierarchy process might be inapplicable when it faces with vague and complex data set. Soft computing methods can be utilized for complicated situations. One of the soft computing methods is a Neuro-Fuzzy algorithm which is used in classification and DM issues. This study has two phases: i) selection of an appropriate fan using Analytical Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and ii) selection of an appropriate pitch angle using Neuro-Fuzzy algorithm and Fuzzy AHP method. This study showed that AHP can be effectively utilized for main fan selection. It performs better than Fuzzy AHP because FAHP contains more expertise and makes problems more complex for evaluating. When FAHP and Neuro-Fuzzy is compared for pitch angle selection, both methodologies yielded the same results. Therefore, utilization of Neuro-Fuzzy in situation with complicated and vague data will be applicable.

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Medeiros, Anderson Vinicius de. "Modelagem de sistemas dinamicos não lineares utilizando sistemas fuzzy, algoritmos geneticos e funções de base ortonormal." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261859.

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Orientadores: Wagner Caradori do Amaral, Ricardo Jose Gabrielli Barreto Campello
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-06T08:36:39Z (GMT). No. of bitstreams: 1 Medeiros_AndersonViniciusde_M.pdf: 896535 bytes, checksum: 48d0d75d38fcbbd0f47f7c49823806f1 (MD5) Previous issue date: 2006
Resumo: Esta dissertação apresenta uma metodologia para a geração e otimização de modelos fuzzy Takagi-Sugeno (TS) com Funções de Base Ortonormal (FBO) para sistemas dinâmicos não lineares utilizando um algoritmo genético. Funções de base ortonormal têm sido utilizadas por proporcionarem aos modelos propriedades como ausência de recursão da saída e possibilidade de se alcançar uma razoável capacidade de representação com poucos parâmetros. Modelos fuzzy TS agregam a essas propriedades as características de interpretabilidade e facilidade de representação do conhecimento. Enfim, os algoritmos genéticos se apresentam como um método bem estabelecido na literatura na tarefa de sintonia de parâmetros de modelos fuzzy TS. Diante disso, desenvolveu-se um algoritmo genético para a otimização de duas arquiteturas, o modelo fuzzy TS FBO e sua extensão, o modelo fuzzy TS FBO Generalizado. Foram analisados modelos locais lineares e não lineares nos conseqüentes das regras fuzzy, assim como a diferença entre a estimação local e a global (utilizando o estimador de mínimos quadrados) dos parâmetros desses modelos locais. No algoritmo genético, cada arquitetura contou com uma representação cromossômica específica. Elaborou-se para ambas uma função de fitness baseada no critério de Akaike. Em relação aos operadores de reprodução, no operador de crossover aritmético foi introduzida uma alteração para a manutenção da diversidade da população e no operador de mutação gaussiana adotou-se uma distribuição variável ao longo das gerações e diferenciada para cada gene. Introduziu-se ainda um método de simplificação de soluções através de medidas de similaridade para a primeira arquitetura citada. A metodologia foi avaliada na tarefa de modelagem de dois sistemas dinâmicos não lineares: um processo de polimerização e um levitador magnético
Abstract: This work introduces a methodology for the generation and optimization of Takagi-Sugeno (TS) fuzzy models with Orthonormal Basis Functions (OBF) for nonlinear dynamic systems based on a genetic algorithm. Orthonormal basis functions have been used because they provide models with properties like absence of output feedback and the possibility to reach a reasonable approximation capability with just a few parameters. TS fuzzy models aggregate to these properties the characteristics of interpretability and easiness to knowledge representation in a linguistic manner. Genetic algorithms appear as a well-established method for tuning parameters of TS fuzzy models. In this context, it was developed a genetic algorithm for the optimization of two architectures, the OBF TS fuzzy model and its extension, the Generalized OBF TS fuzzy model. Local linear and nonlinear models in the consequent of the fuzzy rules were analyzed, as well as the difference between local and global estimation (using least squares estimation) of the parameters of these local models. Each architecture had a specific chromosome representation in the genetic algorithm. It was developed a fitness function based on the Akaike information criterion. With respect to the genetic operators, the arithmetic crossover was modified in order to maintain the population diversity and the Gaussian mutation had its distribution varied along the generations and differentiated for each gene. Besides, it was used, in the first architecture presented, a method for simplifying the solutions by using similarity measures. The whole methodology was evaluated in modeling two nonlinear dynamic systems, a polymerization process and a magnetic levitator
Mestrado
Automação
Mestre em Engenharia Elétrica

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Saleh, Ali Ali Emran. "Development of Machine Learning Techniques for Diabetic Retinopathy Risk Estimation." Doctoral thesis, Universitat Rovira i Virgili, 2020. http://hdl.handle.net/10803/670493.

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La retinopatia diabètica (DR) és una malaltia crònica. És una de les principals complicacions de diabetis i una causa essencial de pèrdua de visió entre les persones que pateixen diabetis. Els pacients diabètics han de ser analitzats periòdicament per tal de detectar signes de desenvolupament de la retinopatia en una fase inicial. El cribratge precoç i freqüent disminueix el risc de pèrdua de visió i minimitza la càrrega als centres assistencials. El nombre dels pacients diabètics està en augment i creixements ràpids, de manera que el fa difícil que consumeix recursos per realitzar un cribatge anual a tots ells. L’objectiu principal d’aquest doctorat. la tesi consisteix en construir un sistema de suport de decisions clíniques (CDSS) basat en dades de registre de salut electrònic (EHR). S'utilitzarà aquest CDSS per estimar el risc de desenvolupar RD. En aquesta tesi doctoral s'estudien mètodes d'aprenentatge automàtic per constuir un CDSS basat en regles lingüístiques difuses. El coneixement expressat en aquest tipus de regles facilita que el metge sàpiga quines combindacions de les condicions són les poden provocar el risc de desenvolupar RD. En aquest treball, proposo un mètode per reduir la incertesa en la classificació dels pacients que utilitzen arbres de decisió difusos (FDT). A continuació es combinen diferents arbres, usant la tècnica de Fuzzy Random Forest per millorar la qualitat de la predicció. A continuació es proposen diverses tècniques d'agregació que millorin la fusió dels resultats que ens dóna cadascun dels arbres FDT. Per millorar la decisió final dels nostres models, proposo tres mesures difuses que s'utilitzen amb integrals de Choquet i Sugeno. La definició d’aquestes mesures difuses es basa en els valors de confiança de les regles. En particular, una d'elles és una mesura difusa que es troba en la qual l'estructura jeràrquica de la FDT és explotada per trobar els valors de la mesura difusa. El resultat final de la recerca feta ha donat lloc a un programari que es pot instal·lar en centres d’assistència primària i hospitals, i pot ser usat pels metges de capçalera per fer l'avaluació preventiva i el cribatge de la Retinopatia Diabètica.
La retinopatía diabética (RD) es una enfermedad crónica. Es una de las principales complicaciones de diabetes y una causa esencial de pérdida de visión entre las personas que padecen diabetes. Los pacientes diabéticos deben ser examinados periódicamente para detectar signos de diabetes. desarrollo de retinopatía en una etapa temprana. La detección temprana y frecuente disminuye el riesgo de pérdida de visión y minimiza la carga en los centros de salud. El número de pacientes diabéticos es enorme y está aumentando rápidamente, lo que lo hace difícil y Consume recursos para realizar una evaluación anual para todos ellos. El objetivo principal de esta tesis es construir un sistema de apoyo a la decisión clínica (CDSS) basado en datos de registros de salud electrónicos (EHR). Este CDSS será utilizado para estimar el riesgo de desarrollar RD. En este tesis doctoral se estudian métodos de aprendizaje automático para construir un CDSS basado en reglas lingüísticas difusas. El conocimiento expresado en este tipo de reglas facilita que el médico pueda saber que combinaciones de las condiciones son las que pueden provocar el riesgo de desarrollar RD. En este trabajo propongo un método para reducir la incertidumbre en la clasificación de los pacientes que usan árboles de decisión difusos (FDT). A continuación se combinan diferentes árboles usando la técnica de Fuzzy Random Forest para mejorar la calidad de la predicción. Se proponen también varias políticas para fusionar los resultados de que nos da cada uno de los árboles (FDT). Para mejorar la decisión final propongo tres medidas difusas que se usan con las integrales Choquet y Sugeno. La definición de estas medidas difusas se basa en los valores de confianza de las reglas. En particular, uno de ellos es una medida difusa descomponible en la que se usa la estructura jerárquica del FDT para encontrar los valores de la medida difusa. Como resultado final de la investigación se ha construido un software que puede instalarse en centros de atención médica y hospitales, i que puede ser usado por los médicos de cabecera para hacer la evaluación preventiva y el cribado de la Retinopatía Diabética.
Diabetic retinopathy (DR) is a chronic illness. It is one of the main complications of diabetes, and an essential cause of vision loss among people suffering from diabetes. Diabetic patients must be periodically screened in order to detect signs of diabetic retinopathy development in an early stage. Early and frequent screening decreases the risk of vision loss and minimizes the load on the health care centres. The number of the diabetic patients is huge and rapidly increasing so that makes it hard and resource-consuming to perform a yearly screening to all of them. The main goal of this Ph.D. thesis is to build a clinical decision support system (CDSS) based on electronic health record (EHR) data. This CDSS will be utilised to estimate the risk of developing RD. In this Ph.D. thesis, I focus on developing novel interpretable machine learning systems. Fuzzy based systems with linguistic terms are going to be proposed. The output of such systems makes the physician know what combinations of the features that can cause the risk of developing DR. In this work, I propose a method to reduce the uncertainty in classifying diabetic patients using fuzzy decision trees. A Fuzzy Random forest (FRF) approach is proposed as well to estimate the risk for developing DR. Several policies are going to be proposed to merge the classification results achieved by different Fuzzy Decision Trees (FDT) models to improve the quality of the final decision of our models, I propose three fuzzy measures that are used with Choquet and Sugeno integrals. The definition of these fuzzy measures is based on the confidence values of the rules. In particular, one of them is a decomposable fuzzy measure in which the hierarchical structure of the FDT is exploited to find the values of the fuzzy measure. Out of this Ph.D. work, we have built a CDSS software that may be installed in the health care centres and hospitals in order to evaluate and detect Diabetic Retinopathy at early stages.

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Escudero, Vasconez Maria Veronica. "Design and Delivery of Effective Activation Measures : what Works and for Whom?" Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEH122.

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Les politiques actives du marché du travail (PAMT) sont considérées de plus en plus comme nécessaires pour renforcer le lien entre protection sociale et création de sources de revenu plus durables dans l’objectif d’améliorer la qualité de l’emploi mais aussi, de façon plus générale, les conditions de vie. En conséquence, ces mesures jouent un rôle essentiel aujourd’hui dans les programmes de politique publique de la plupart des économies avancées et voient leur importance augmenter fortement dans les pays émergents et dans les pays en développement, où elles ne sont pas encore aussi bien établies. Il reste toutefois encore beaucoup à apprendre sur l’impact de ces mesures, en particulier sur le rôle des caractéristiques de leur mise en œuvre. Cette thèse entend contribuer à ce débat en étudiant l’efficacité des PAMT et le rôle des systèmes de mise en œuvre pour ce qui est de leur impact à la fois dans les pays développés et dans les pays émergents et en développement.Le premier chapitre examine sous un angle macroéconomique l’efficacité des PAMT à améliorer les résultats sur le marché du travail au sein des pays de l’OCDE, en particulier pour les travailleurs peu qualifiés. Il est capital de saisir de façon empirique l’effet net global des PAMT sur l’ensemble du marché du travail, car ces politiques entraînent souvent des phénomènes de substitution, de déplacement et d’autres conséquences indirectes. Les deux chapitres suivants cherchent à déterminer si les PAMT doivent être encore étendues dans les pays émergents et en développement. Pour ce faire, les effets au niveau individuel de deux types de PAMT en Amérique latine sont étudiés, en s’appuyant sur la présence de règles d’attribution intéressantes et de données de qualité au niveau individuel. Ainsi, le deuxième chapitre s’intéresse plus particulièrement aux conséquences à moyen et long terme d’un programme de workfare péruvien, l’une des mesures d’activation les moins étudiées, bien que fréquemment mise en œuvre dans la région, afin d’évaluer la durabilité de ses effets. Le troisième chapitre s’intéresse à l’introduction d’un ensemble complet de PAMT en Argentine, dans le but d’aider les bénéficiaires éligibles d’un programme de transfert de fonds sous conditions à trouver des sources de revenus plus stables. Dans les deux cas, l’accent est mis sur les effets sur la qualité de l’emploi et sur la manière dont la mise en œuvre des mesures conditionne leur impact.Ces travaux montrent que les PAMT sont utiles mais à condition qu’elles s’accompagnent d’une conception et d’une mise en œuvre appropriées. Les résultats confirment l’importance de ces facteurs pour ce qui est de l’efficacité des mesures tant dans les pays de l’OCDE que dans ceux d’Amérique latine étudiés. L’ampleur des effets dépend du type de mesure étudiée et de la catégorie de bénéficiaires visée
Today, active labor market policies (ALMPs) are increasingly seen as a necessary tool to strengthen the link between social protection and the creation of more sustainable sources of income with a view to increasing work quality but also improving living conditions more broadly. As a result, the role of ALMPs in policy agendas remains high in most advanced economies and has increased dramatically in emerging and developing countries, where ALMPs are still less established. Despite this, there is still a lot to be learned regarding the impact of these policies, particularly with regards to the role of implementation characteristics. My dissertation aims to contribute to this debate by looking at the effectiveness of ALMPs and the role of delivery systems in shaping their impact in both, developed and emerging and developing countries.It starts by examining the effectiveness of ALMPs in OECD countries in improving labor market outcomes, especially for low-skilled individuals, from a macroeconomic perspective (Chapter 1). Capturing empirically the overall net effect of ALMPs on the wide labor market is of upmost importance, since the role of ALMPs frequently involves substitution, displacement and other indirect effects. Then, the following two chapters aim to assess whether ALMPs should be leveraged further in emerging and developing countries, by investigating the individual-level effects of two different types of ALMPs in Latin America, exploiting the availability of interesting assignment rules and good-quality individual-level data. Chapter 2 focuses on the medium- to long-term effects of a Peruvian workfare program, one of the least studied ALMPs in the region albeit commonly implemented, to assess the sustainability of these type of programs’ effects. Chapter 3 then looks at the provision of a comprehensive package of ALMPs in Argentina, implemented to support eligible beneficiaries of a conditional cash transfer program in finding more stable income opportunities. In both cases, the focus is placed on the effects on work quality and on the role of design and implementation in shaping the effects.My research suggests that ALMPs are relevant but mostly through appropriate design and implementation aspects. The results confirm the importance of these factors in ensuring effectiveness both in OECD and the Latin American countries assessed. The size of effects depends on the type of policy assessed and on the beneficiary group

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Clara, i. Lloret Narcís. "Estudi de mètodes de classificació borrosa i la seva aplicació a l'agrupació de zones geogràfiques en base a diverses característiques incertes." Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7755.

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Aquesta memòria està estructurada en sis capítols amb l'objectiu final de fonamentar i desenvolupar les eines matemàtiques necessàries per a la classificació de conjunts de subconjunts
borrosos. El nucli teòric del treball el formen els capítols 3, 4 i 5; els dos primers són dos capítols de caire més general, i l'últim és una aplicació dels anteriors a la classificació dels
països de la Unió Europea en funció de determinades característiques borroses.
En el capítol 1 s'analitzen les diferents connectives borroses posant una especial atenció en aquells aspectes que en altres capítols tindran una aplicació específica. És per aquest motiu que s'estudien les ordenacions de famílies de t-normes, donada la seva importància en la transitivitat de les relacions borroses. La
verificació del principi del terç exclòs és necessària per assegurar que un conjunt significatiu de mesures borroses generalitzades, introduïdes en el capítol 3, siguin reflexives.
Estudiem per a quines t-normes es verifica aquesta propietat i introduïm un nou conjunt de t-normes que verifiquen aquest principi.
En el capítol 2 es fa un recorregut general per les relacions borroses centrant-nos en l'estudi de la clausura transitiva per a qualsevol t-norma, el càlcul de la qual és en molts casos
fonamental per portar a terme el procés de classificació. Al final del capítol s'exposa un procediment pràctic per al càlcul d'una
relació borrosa amb l'ajuda d'experts i de sèries estadístiques.
El capítol 3 és un monogràfic sobre mesures borroses. El primer objectiu és relacionar les mesures (o distàncies) usualment utilitzades en les aplicacions borroses amb les mesures
conjuntistes crisp. Es tracta d'un enfocament diferent del tradicional enfocament geomètric. El principal resultat és la introducció d'una família parametritzada de mesures que verifiquen
unes propietats de caràcter conjuntista prou satisfactòries.
L'estudi de la verificació del principi del terç exclòs té aquí la seva aplicació sobre la reflexivitat d'aquestes mesures, que són
estudiades amb una certa profunditat en alguns casos particulars.
El capítol 4 és, d'entrada, un repàs dels principals resultats i mètodes borrosos per a la classificació dels elements d'un mateix
conjunt de subconjunts borrosos. És aquí on s'apliquen els resultats sobre les ordenacions de les famílies de t-normes i t-conormes estudiades en el capítol 1. S'introdueix un nou mètode
de clusterització, canviant la matriu de la relació borrosa cada vegada que s'obté un nou clúster. Aquest mètode permet homogeneïtzar la metodologia del càlcul de la relació borrosa amb
el mètode de clusterització.
El capítol 5 tracta sobre l'agrupació d'objectes de diferent naturalesa; és a dir, subconjunts borrosos que pertanyen a diferents conjunts. Aquesta teoria ja ha estat desenvolupada en el
cas binari; aquí, el que es presenta és la seva generalització al cas n-ari. Més endavant s'estudien certs aspectes de les projeccions de la relació sobre un cert espai i el recíproc,
l'estudi de cilindres de relacions predeterminades. Una aplicació sobre l'agrupació de les comarques gironines en funció de certes
variables borroses es presenta al final del capítol.
L'últim capítol és eminentment pràctic, ja que s'aplica allò estudiat principalment en els capítols 3 i 4 a la classificació dels països de la Unió Europea en funció de determinades
característiques borroses. Per tal de fer previsions per a anys venidors s'han utilitzat sèries temporals i xarxes neuronals.
S'han emprat diverses mesures i mètodes de clusterització per tal de poder comparar els diversos dendogrames que resulten del procés
de clusterització.
Finalment, als annexos es poden consultar les sèries estadístiques utilitzades, la seva extrapolació, els càlculs per a la construcció de les matrius de les relacions borroses, les matrius
de mesura i les seves clausures.
This thesis is organized in six chapters with the final goal to found and explain the mathematical set of tools necessary to classify sets of fuzzy sets. The theoretic kernel is made by the chapters 3, 4 and 5; the first and second are more generals and the last one is an aplication of the precedent to make a classification of the union european countries in function of some vague attibutes.
In the first chapter we analize the different fuzzy logic connectives making a special attention those aspects which will have a specific application in other chapters. Is for this reason that we study the order of families of t-norms, given its importance in the transivity of fuzzy relations. The verification of the third excluded principle is necessary to ensure that a significant set of generalized fuzzy measures, introduced in the chapter 3, were reflexive. We study for which t-norms is verified this property and we introduce a new set of t-norms which verify this principle.
In the second chapter we study in a general way the fuzzy relations making a special attention in the transivity closure for any t-norm, its calculus is in a lot of cases basic to make the classification process. At the end of this chapter we describe a practical method to find a fuzzy relation with the help of experts and statistical series.
The third chapter is a monographic about fuzzy measures. The first goal is to relate the measures (or distances) usually used in the fuzzy applications with the crisp measures. The question is to change the traditional geometrical point of view for another absolutely fuzzy. The first result is the introduction of a parametrized family of measures that verify a set of properties enough satisfactories. The study of the third exclude principle has here its application about the reflexivity of these measures which are studied with certain profundity in some particular cases.
The fourth chapter is, at the beginning, a review of the main results and fuzzy methods for the classification of elements of a same set of fuzzy sets. Is now where we apply the results of orders for t-norms and t-conorms studied in the first chapter. We introduce a new method of fuzzy clustering, changing the fuzzy relation matrix each time that we obtain a new cluster. This method permit to homogenize the methodology of the calculus of the fuzzy relation with the clustering method.
The fifth chapter is about the objects association of different nature; that is, fuzzy subsets that belong to different sets. This theory already has been developed in the binary case; here, we submit its generalization for the n dimensional case. Later, we study certain aspects of the fuzzy relation projection on a certain space and the reciprocal, the cilindrical extensions. An application about grouping regions of Girona in function of some uncertain attibutes finish the chapter.
The last chapter is eminently applied, because we apply that studied in the 3 and 4 chapters to classify the union european countries in function of some fuzzy attributes. To do forecasts for coming years we have used time series and neural networks. We have used several measures and clustering methods in order to compare the dendograms that result of the clustering process.
Finally, in the suplements we can consult the used time series, its extrapolation, the calculus to construct the fuzzy relations, the measure matrixs and its closures.

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Abdel-Jaber, Hussein F. "Performance Modelling and Evaluation of Active Queue Management Techniques in Communication Networks. The development and performance evaluation of some new active queue management methods for internet congestion control based on fuzzy logic and random early detection using discrete-time queueing analysis and simulation." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4261.

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Since the field of computer networks has rapidly grown in the last two decades, congestion control of traffic loads within networks has become a high priority. Congestion occurs in network routers when the number of incoming packets exceeds the available network resources, such as buffer space and bandwidth allocation. This may result in a poor network performance with reference to average packet queueing delay, packet loss rate and throughput. To enhance the performance when the network becomes congested, several different active queue management (AQM) methods have been proposed and some of these are discussed in this thesis. Specifically, these AQM methods are surveyed in detail and their strengths and limitations are highlighted. A comparison is conducted between five known AQM methods, Random Early Detection (RED), Gentle Random Early Detection (GRED), Adaptive Random Early Detection (ARED), Dynamic Random Early Drop (DRED) and BLUE, based on several performance measures, including mean queue length, throughput, average queueing delay, overflow packet loss probability, packet dropping probability and the total of overflow loss and dropping probabilities for packets, with the aim of identifying which AQM method gives the most satisfactory results of the performance measures. This thesis presents a new AQM approach based on the RED algorithm that determines and controls the congested router buffers in an early stage. This approach is called Dynamic RED (REDD), which stabilises the average queue length between minimum and maximum threshold positions at a certain level called the target level to prevent building up the queues in the router buffers. A comparison is made between the proposed REDD, RED and ARED approaches regarding the above performance measures. Moreover, three methods based on RED and fuzzy logic are proposed to control the congested router buffers incipiently. These methods are named REDD1, REDD2, and REDD3 and their performances are also compared with RED using the above performance measures to identify which method achieves the most satisfactory results. Furthermore, a set of discrete-time queue analytical models are developed based on the following approaches: RED, GRED, DRED and BLUE, to detect the congestion at router buffers in an early stage. The proposed analytical models use the instantaneous queue length as a congestion measure to capture short term changes in the input and prevent packet loss due to overflow. The proposed analytical models are experimentally compared with their corresponding AQM simulations with reference to the above performance measures to identify which approach gives the most satisfactory results. The simulations for RED, GRED, ARED, DRED, BLUE, REDD, REDD1, REDD2 and REDD3 are run ten times, each time with a change of seed and the results of each run are used to obtain mean values, variance, standard deviation and 95% confidence intervals. The performance measures are calculated based on data collected only after the system has reached a steady state. After extensive experimentation, the results show that the proposed REDD, REDD1, REDD2 and REDD3 algorithms and some of the proposed analytical models such as DRED-Alpha, RED and GRED models offer somewhat better results of mean queue length and average queueing delay than these achieved by RED and its variants when the values of packet arrival probability are greater than the value of packet departure probability, i.e. in a congestion situation. This suggests that when traffic is largely of a non bursty nature, instantaneous queue length might be a better congestion measure to use rather than the average queue length as in the more traditional models.

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Ivana, Štajner-Papuga. "Uopštena konvolucija." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2001. https://www.cris.uns.ac.rs/record.jsf?recordId=5987&source=NDLTD&language=en.

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U ovoj tezi je definisana uopštena konvolucija koja pripada domenu pseudo-analize i ima veliku primenu u mnogim matematičkim teorijama, npr. u proba-bilističkim metričkim prostorima, PDJ, teorijama odlučivanja, sistema, kontrole i fazi brojeva. Dokazane su bitne osobine ove operacije sa funkcijama. Dokazana je veza izmedju pseudo-konvolucija baziranih na poluprstenima različitih klasa Definisana je (5, C/)-konvolucija bazirana na uslovno distributivnom poluprstenu ([0,1], S, U)).Dat je još jedan vid uopštenja konvolucije baziran na uopštenim pseudo-operacijama.
In this thesis the generalized convolution have been defined. This operation with functions has applications in different mathematical theo ries, for example in Probabilistic Metric Spaces, PDE, System and Control Theory, Fuzzy numbers. Some basic properties of this operation has been proved, as well as connection between generalized convolutions based on different classes of semirings. (5, U)-convolution has been defined, as well as convolution based on generalized pseudo-operations.

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Chandran, Gautam David. "The development of a fuzzy semantic sentence similarity measure." Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/617190/.

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A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent the effect perception based (fuzzy) words, which are commonly used in natural language, have on sentence similarity. This research project developed a new sentence similarity measure to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is a novel ontology-based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words contained within them and from that a new fuzzy ontology was created. Using these relationships allows for the creation of a new ontology-based fuzzy semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. In order to evaluate FAST, two new test datasets were created through the use of questionnaire based human experimentation. This involved the generation of a robust methodology for creating usable fuzzy datasets (including an automated method that was used to create one of the two fuzzy datasets). FAST was evaluated through experiments conducted using the new fuzzy datasets. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures demonstrating its success in representing the similarity between pairs of sentences containing fuzzy words.

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30

Assi, Jolnar Abdulkarim. "Knightian uncertainty modelling and its impact on option pricing : applications of fuzzy set theory, fuzzy measure theory and fuzzy differential calculus." Thesis, City University London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274460.

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31

Varghese, Rency Susan. "Confidence Measure for DNA Base Calling Using a Fuzzy System." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/VargheseRS2004.pdf.

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32

Fischer, Manfred M., and Josef Benedikt. "The Use of Fuzzy Set Theory in Remote Sensing Pattern Recognition." WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/4174/1/WSG_DP_5096.pdf.

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Satellite images increasingly become a major data source for monitoring changes in the natural environment. A main task in the analysis of satellite images is concerned with the modelling of land use classes by reducing uncertainty during a classification process. In the approach presented in this paper uncertainty is perceived to be due to the vagueness of geographical categories caused by either the complexity of the category (like 'urban area') or by the use of the category in several application contexts. Two circumstances of use of an extended set theoretical concept (fuzzy sets) are discussed. First, two algorithms from the ISODATA class are used to determine the grades of membership to three a priori defined classes (woodland, suburban area, urban area) of a LANDSAT TM satellite image of Vienna, Austria. The results are visualized by a RGB image of the grades of membership to each category. Second, a measure of fuzziness is employed on the results. The measure relies on the concept of distance between a seUcategory and its complement. The so determined vagueness provide more information on the uncertainty of the different categories and may improve further information processing tasks. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience

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33

Lo, Yi-Chen. "Detection of gas/odor based on quartz crystal microbalance sensors and fuzzy similarity measure." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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34

Chen, Yen-He, and 陳彥合. "Developing Decision Weights by Fuzzy Number-Valued Fuzzy Measures." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/ezzdze.

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碩士
長庚大學
企業管理研究所
95
Under current environment, the single attribute decision method can’t match the reality any more. When facing multiple criteria decision making (MCDM), the importance of each attribute varies. That makes the matter of how to appraise each attribute become more important. The different degrees of significance change the consequences of selection. That makes the set up of the weight become very important when we facing MCDM. Therefore, this research will categorize and illustrate the present decision weight by reviewing documents. In normal decision weight, the most frequent question about attribute is too many hypotheses. For example, it has to match independence, additive…and so on. Based on facing the problems of over hypothesis, some scholars suggested to measure weight by Fuzzy Measures. After the experiments of this research, we found out that if we calculate weight by fuzzy measures tends to have a phenomenon of Positive Leniency. Therefore, this research based on Fuzzy Measures to develop decision weight by fuzzy numbers to improve the phenomenon of Positive Leniency. The biggest difference between these two methods lies in that ZFM uses the concept of distance to show the difference of importance among each attribute and get triangular fuzzy number of each attribute by the distance and use different α-cut value to obtain different fuzzy numbers. After the Choquet integration, we can have project evaluation. After confirm the research method, we can compare to project evaluation by AHP, FM and ZFM. This research will divided products into 3 categories: Functional, Hedonic and Hybrid. Pick two products from each category to proceed the project evaluation. Products we choose are: shampoo, printer, KTV store, cinema, note book and jeans. There are 3 methods:AHP, FM and ZFM. Based on these 3 methods, this research uses 4 principals to compare to each other: data base, difficulty of calculation, Hit ratio of alternative priority and positive leniency. We found out that in the aspect of data base. When the attribute number and , HP needs the biggest data base among FM and ZFM. When attribute number n become bigger, then AHP needs more data base. In the aspect of difficulty of calculation, AHP has more difficult calculation procedure while ZFM and FM haven’t. In the aspect of Hit ratio of alternative priority, each one is: AHP－58.20%、FM－57.87%及ZFM－66.85%, Therefore, in the method of this: ZFM AHP FM. In the aspect of positive leniency, none of FM or ZFM can completely decide examiner toward to the phenomenon of positive leniency but ZFM can make this question become better. Therefore, no matter in the aspect of data base, difficulty of calculation or Hit ratio of alternative priority, ZFM is better than AHP. We hope that as the development of ZFM goes, we can make right decisions when the decision maker faces a situation of multi-attribute problems.

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Yun-Lun, Tsai, and 蔡昀倫. "New Fuzzy-Number Similarity Measures for Fuzzy Clustering Problems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/86716547251835793537.

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碩士
國立聯合大學
資訊管理學系碩士班
102
This study propose two methods for measuring the degree of similarity with generalized fuzzy numbers and interval-valued fuzzy numbers based on mean mapping distance ratio and mean absolute deviation. Some properties of the methods are demonstrated, then 56 sets of generalized fuzzy numbers and 25 sets of interval-valued fuzzy numbers are adopted to compare the proposed methods with the existing methods. The results indicated that the proposed methods are better than existing methods. Furthermore, we propose a fuzzy number clustering method using the proposed methods of similarity measure, and then we analyzed four literatures and compared the proposed clustering method with the existing methods. Comparative results indicated that the proposed method can overcome the drawbacks of the existing methods. A numerical example is demonstrated the new mechanism.

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Shie, Jen-Da, and 謝政達. "New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/93504009282990968312.

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碩士
國立臺灣科技大學
資訊工程系
94
Classification techniques have been widely applied in many domains. In this thesis, we propose two new methods for handling classification problems. The first method selects feature subsets for handling classification problems based on fuzzy entropy measures focusing on boundary samples. It can deal with both numeric and nominal features. It can select relevant features to get higher average classification accuracy rates than the ones selected by the existed methods. The second method handles classification problems based on fuzzy information gain measures. It can deal with both numeric and nominal features. It can get higher average classification accuracy rates than the existed methods.

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Hsiao, Chien-Lan, and 蕭建蘭. "Similarity Measures in Fuzzy Regression Models." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/74145993398701467870.

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Wu, Mei-Ching, and 伍美菁. "On Semantic Measures of Fuzzy Data in Fuzzy Relational Databases." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/76728890066867422258.

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碩士
元智大學
資訊管理學系
90
Function Depenendcy stands on the semantic relationship on relational database, and the basis to the normalized of data. The research on assessing data semantic relationship between fuzzy data in fuzzy relational databases has been undertaken with respect to various fuzzy models recently. This paper proposes an approach for evaluating semantic relationship between fuzzy data in relational databases, where the fuzziness of data is modeled in both attribute values and attribute domain elements. Particularly, the attribute values are presented by a possibility distribution, and are applied on attribute domain elements. Otherwise, the properties of fuzzy data with Semantic Proximxity are also included, where the correctness and soundness are proved that inferring to the relative theorem of Fuzzy Functional Dependency（FFD）. Finally, it is supposed to implement the prototype system of " the analysis of Fuzzy Data similarity " and to calculate Semantic Proximity to determined the similarity.

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chin-chun, chen, and 陳進春. "Theory and Application of the Composed Fuzzy Measure of L-Measure and Delta-Measures." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/80250545345543737651.

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博士
國立臺中教育大學
教育測驗統計研究所
98
When there are interactions among independent variables, traditional multiple linear regression models do not perform well enough. The traditional improved methods exploited the ridge regression models. In this paper, we suggest using the Choquet integral regression models based on some single or compounded fuzzy measures to improve this situation. The well-known fuzzy measures, -measure and P-measure, have only one formulaic solution. Two multivalent fuzzy measures with infinitely many solutions were proposed by our previous works, called L-measure and -measure, but L-measure do not include the additive measure and -measure has not so many measure solutions as L-measure. Due to the above drawbacks, an improved fuzzy measure composed of the above two multivalent fuzzy measures, denoted -measure, is developed. For evaluating the Choquet integral regression models with our developed fuzzy measure and other different ones, two real data experiment by using a 5-fold cross-validation mean square error (MSE) is conducted. The performances of Choquet integral regression models with fuzzy measure based on respective -measure, L-measure, -measure, -measure, and P-measure, a ridge regression model, and a multiple linear regression model are compared. Experimental result shows that the Choquet integral regression models with respect to -measure based on -support outperforms others forecasting models.

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40

Wang, ZhiYong, and 王智永. "Design New Fuzzy-Number Similarity Measures and Fuzzy-Number Clustering Method." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/41112788594070593554.

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碩士
國立聯合大學
資訊管理學系碩士班
100
This study proposes new methods based on map distance to measure the degree of similarity between generalized and interval-valued fuzzy numbers. Some properties of the proposed similarity measures are demonstrated here. There are 50 sets of generalized fuzzy numbers and 21 sets of interval-valued fuzzy numbers are adopted to compare the proposed methods with some existing similarity measures for proving the proposed similarity measures are better than the existing methods. Furthermore, the proposed similarity measure is used to deal with fuzzy-number cluster problems. We present a new method for handling the fuzzy clustering problems of which the characteristic values and weights of indices are generalized fuzzy numbers. The proposed mechanism is based on the fuzzy-number similarity measure. Firstly determine the linguistic evaluating values and the linguistic weights of each evaluating criterion with respect to the alternatives. Then measure the degree of similarity between two arbitrary weighted evaluating values on the same criterion. Finally constructing the hierarchical cluster tree and generated different clusters. A numerical example is demonstrated the new mechanism.

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41

Lin, Der-Chen, and 林德成. "On measures of type-2 fuzzy sets." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/42726357119000209383.

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博士
中原大學
應用數學研究所
96
Abstract In a practical complex system, humans sometimes use only binary logic theory for deducing some objects or information which is not sufficient to explain all situations. Thus, a fuzzy concept can be utilized for assisting deductions. As for some unclear, uncertain, and incomplete information, they can be compared and screened by measured value of fuzzy set. Additionally, the new definition and theorem of type-2 fuzzy sets proposed by Mendel and John in recent years have been widely studied and spread, and applied to many fields. This dissertation presents a relative definition of measurement of fuzzy degree, inclusion degree and similarity degree to type-2 fuzzy sets, and discusses certain relativity and properties among them. Illustrations for practical demand are used to show how to calculate the measurement of fuzzy degree, inclusion degree and similarity degree among type-2 fuzzy sets. Furthermore, in the discussion, the algorithm of Yang and Shish is used as a method for cluster analysis, and comparison is made with the results of Hung and Yang. According to different α-levels, these cluster results are reasonably included in a hierarchical tree.

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Li, Chia Hang, and 李佳航. "Accessing Informational Importance Using Intuitionistic Fuzzy Entropy Measures." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/10228714901450788932.

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碩士
長庚大學
企業管理研究所
96
In Multiple Attribute Decision Making (MADM), it is important to properly assess the attribute weight, because different weight result would often cause entirely different decision result. Furthermore, after the IFS was applied to solve MADM problems, it causes our data and decision matrix get more complex and contain more uncertainty, and therefore it is relatively important to make sure of the credibility of data itself. However, there is little investigation on MADM with the credibility of data being explicitly taken into account in the past. In our research, we propose a new objective weight method by using IF entropy measures for MADM under intuitionistic fuzzy environment. We utilize the nature of IF entropy to assess the attribute weight based on the credibility of data. Moreover, there were many IF entropy measures which were originated with different theories, and we also investigate the differences among those varied measures. According to the experiment result, the differences undoubtedly exist among those measures. Even the measures which were originated from the same theory also contain variation among them. Besides, we also understand the number of attributes and alternatives would influence the degree of difference among those measures.

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43

Wei, Shih-Hua, and 魏世驊. "New Methods for Fuzzy Risk Analysis Based on Similarity Measures between Fuzzy Numbers." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/am43ea.

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碩士
國立臺灣科技大學
資訊工程系
95
In recent years, the task of measuring the degree of similarity between fuzzy numbers plays an important role in fuzzy decision making, information fusion and fuzzy risk analysis. In this thesis, we present two similarity measures for generalized fuzzy numbers and interval-valued fuzzy numbers. It combines the concepts of geometric distance, the perimeter, height and center of gravity point of generalized fuzzy numbers and interval-valued fuzzy number, respectively. Moreover, we also presented an interval-valued fuzzy number adjusting algorithm. Based on proposed similarity measures, we propose two new methods for handling fuzzy risk analysis problems. The proposed fuzzy risk analysis methods can overcome the drawback of existing methods. They can deal with fuzzy risk analysis in a more intelligent and flexible manner.

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44

Jiun, Ku Tai, and 古苔均. "Determining attribute importance based on triangular and trapezoidal fuzzy numbers in (z)fuzzy measures." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/16831452245780676924.

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碩士
長庚大學
企業管理研究所
95
Weight is one of the most useful tools and it becomes the core of decision-making and the center of a method for measuring attribute importance. Besides, weight method is often measured by fuzzy numbers. Triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) of the types are the most easily and simply of GLRFN. Moreover, we consider the value of fuzzy measures as a linguistic value and then convert linguistic terms to fuzzy numbers. (z)fuzzy measures constructs membership functions that adequately capture the meanings of linguistic terms. The purpose of this study is to use (z)fuzzy measures to determine attribute importance. Then, we use different fuzzy numbers such as TFNs and TrFNs to analyze the result of the empirical study. Besides, we also use different distance measures to adjust attribute importance. In TFNs, we selected five types about five-scales of fuzzy linguistic scale. In TrFNs, we also selected three types about five-scales of fuzzy linguistic scale. First, respondents will choice the most important attribute and then distance measures can adjust between the most important attribute and others. So, we can get the TFN and TrFN of every attribute. Later, we used (z)fuzzy measures to obtain attribute importance. In next step, we will calculate (z)fuzzy integrals for all alternatives and use centre-index method to get a crisp number. Finally, we establish a priority of alternatives with respondents using Spearman rank-order correlation coefficient, the consistency of the best alternative and the consistency of the better alternative. We collect sixteen samples from college and graduate school. The result showed three points. First, there are no significant difference between five scales of TFNs and three scales of TrFNs. Second, TFNs obtain similar analysis result to TrFNs. Therefore, we can not use more complex TrFNs to determine attributes importance. Finally, there are no significant differences between three distance measures.

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Kao, Hsiao-Wei, and 高曉薇. "New Fuzzy-Number Similarity Measures and Prioritized Information Fusion Mechanisms for Fuzzy Recommendation Problems." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/20907517971403212545.

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碩士
國立聯合大學
管理碩士學位學程
98
In this thesis, we propose a new method for measuring the degree of similarity between generalized fuzzy numbers based on standard deviation. Some properties of the proposed similarity measure are demonstrated, and 44 sets of generalized fuzzy numbers are applied to compare the proposed method with existing similarity measures. Furthermore, the proposed similarity measure is used to solve fuzzy recommendation problems. A decision maker’s evaluations of parameters or variables involve with real-world problems that can be represented by interval-valued fuzzy numbers. Therefore, we also present a new similarity measured method that based on the standard deviation operator to solve before similarity measurement between interval-valued fuzzy numbers. In addition, some properties of the proposed similarity measure have been demonstrated, and 17 sets of interval-valued fuzzy numbers are adopted to compare the proposed method with existing similarity measures. Furthermore, the proposed similarity measure is used to deal with fuzzy recommendation problems. Chen and Chen [23] and Hong et. al [40] presented new prioritized information fusion algorithm for handling fuzzy information retrieval problems. However, according to our research, these algorithms still have the following drawbacks. Thence, we present a new prioritized information fusion algorithm based on based on GMA operator and fuzzy-number similarity measure to deal with prioritized multi-criteria fuzzy decision-making problems and prioritized information filtering problems based on generalized fuzzy numbers. However, some researchers have pointed out that using interval-valued fuzzy numbers for representing linguistic terms improves flexibility. Thence, we also present a new prioritized information fusion algorithm for handling information filtering problems based on interval-valued fuzzy numbers. Furthermore, we use the proposed fusion algorithm for handling information filtering problems. The proposed prioritized information fusion algorithm can deal with information filtering problems in a more flexible manner.

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46

Li, Hou Hsun, and 李後勳. "A Study of Intuitionistic Fuzzy Similarity Measures on Group Decision Making." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/55025887950799017965.

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碩士
長庚大學
企業管理研究所
97
In 1965, fuzzy sets were introduced by Zadeh to deal with the imprecise and variable data in real life. In several decades later, intuitionistic fuzzy set theory were developed by Atanassov to express the human feeling with numerical numbers, and were proposed in group decision making. One practical application is similarity measures. Naturally, at the beginning of every group decision making problem, experts’ opinions may differ substantially. Therefore, it is necessary to develop a consensus process in an attempt to obtain the maximum degree of consensus or agreement between the set of experts on the solution set of alternatives. Consensus model process is processed to integrate a single opinion into group opinions, which could be ranked by numerical numbers. The aim of this paper is to present a consensus model for solving group decision making problems in intuitionistic fuzzy set environment. We conduct the proposed method on different similarity measures to discuss the effect to the ranking result. The similarity measures are separated into four groups from each similarity measure expression and its own measuring focus. In addition, a comprehensive experimental analysis to observe the intuitionistic fuzzy consensus results yielded by different similarity measures is presented. Several comparison indices are examined, including the average Spearman correlation coefficients, the consistency rate, the inversion rate, and the contradiction rate. According to the results, the four indices are affected as the number of decision alternatives in a problem increases. On the other hand, the number of attributes, and the number of experts have only a minor practical influence in view of the four indices. We suggest forward researchers that it might be a better choice to apply a simple and measure.

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47

Liao, Z.-Han, and 廖姿涵. "On similarity, inclusion and entropy measures between interval-valued fuzzy sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/88412490306171034612.

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碩士
中原大學
應用數學研究所
103
Interval-valued fuzzy sets (IVFSs) are an extension of fuzzy sets. Since IVFSs present fuzzy sets with interval-valued memberships, they could have more widely for uncertainty modeling than fuzzy sets and are also easier to handle in practice than type-2 fuzzy sets. It is known that similarity, inclusion and entropy are the three important measures for fuzzy concepts. In this paper, we first propose new inclusion measures for IVFSs. We then derive similarity measures between IVFSs based on these inclusion measures. Furthermore, we take a weighted average of these two similarity measures between IVFSs and then construct new entropy measures for IVFSs. Some properties of these new similarity, inclusion and entropy measures between IVFSs are made. We also make numerical comparisons of the proposed measures with some existing measures. These comparison results show the superiority of the proposedmeasures.

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48

LIN, SHYI-CHYUAN, and 林錫泉. "A study in the sequencing problem with multiple fuzzy performance measures." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/57982391139681474451.

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Chen, Chia-Ling, and 陳佳伶. "New Fuzzy Interpolative Reasoning Methods Based on Ranking Values of Polygonal Fuzzy Sets, Automatically Generated Weights of Fuzzy Rules and Similarity Measures Between Polygonal Fuzzy Sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47496074659379726144.

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Abstract:

碩士
國立臺灣科技大學
資訊工程系
103
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and the ranking values of polygonal fuzzy sets. In the first method of our thesis, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. The experimental results show that the proposed method can overcome the drawbacks of the existing fuzzy interpolative reasoning methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive fuzzy interpolation method based on ranking values of polygonal fuzzy sets and similarity measures between polygonal fuzzy sets. The proposed adaptive fuzzy interpolation method performs fuzzy interpolative reasoning using multiple fuzzy rules with multiple antecedent variables and solves the contradictions after the fuzzy interpolative reasoning processes based on similarity measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolation method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

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50

Chen, Jim-Ho, and 陳進和. "New Methods for Fuzzy Risk Analysis Based on Ranking Generalized Fuzzy Numbers and Similarity Measures between Interval-Valued." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/3h94qt.

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Abstract:

碩士
國立臺灣科技大學
資訊工程系
95
In this thesis, we present two methods for fuzzy risk analysis based on ranking generalized fuzzy numbers and similarity measures between interval-valued fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers for handling fuzzy risk analysis problems. The proposed method considers defuzzified values, the weight and the spreads of generalized fuzzy numbers. Moreover, we also apply the proposed method for ranking generalized fuzzy numbers to present a new method for dealing with fuzzy risk analysis problems. Then, we present a new similarity measure for interval-valued fuzzy numbers. The proposed similarity measure considers five factors, i.e., the degree of similarity on X-axis between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity about the weight of the upper fuzzy numbers of the interval-valued fuzzy numbers, the spread between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity on the X-axis between the interval-valued fuzzy numbers, and the degree of similarity on the Y-axis between the interval-valued fuzzy numbers. Moreover, we also present new interval-valued fuzzy numbers arithmetic operators and apply the proposed similarity measure to present a new method for dealing with fuzzy risk analysis problems based on interval-valued fuzzy numbers. The proposed fuzzy risk analysis methods provide us a useful way for handling fuzzy risk analysis problems.

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Dissertations / Theses on the topic 'Fuzzy Measures'

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