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Mao, Hongwei. "Estimating labour productivity using fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0019/MQ47065.pdf.
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Raghfar, Hossein. "Application of fuzzy set theory to poverty analysis." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343582.
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Saboo, Jai Vardhan. "An investment analysis model using fuzzy set theory." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/50087.
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Traditional methods for evaluating investments in state-of-the-art technology are sometimes found lacking in providing equitable recommendations for project selection. The major cause for this is the inability of these methods to handle adequately uncertainty and imprecision, and account for every aspect of the project, economic and non-economic, tangible and intangible. Fuzzy set theory provides an alternative to probability theory for handling uncertainty, while at the same time being able to handle imprecision. It also provides a means of closing the gap between the human thought process and the computer, by enabling the establishment of linguistic quantifiers to describe intangible attributes. Fuzzy set theory has been used successfully in other fields for aiding the decision-making process.
The intention of this research has been the application of fuzzy set theory to aid investment decision making. The research has led to the development of a structured model, based on theoretical algorithms developed by Buckley and others. The model looks at a project from three different standpoints- economic, operational, and strategic. It provides recommendations by means of five different values for the project desirability, and results of two sensitivity analyses. The model is tested on a hypothetical case study. The end result is a model that can be used as a basis for promising future development of investment analysis models.Master of Science
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Bhattacharyya, Kakali. "Classification of rock masses based on fuzzy set theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29490352.
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Murali, V. "A study of universal algebras in fuzzy set theory." Thesis, Rhodes University, 1988. http://hdl.handle.net/10962/d1001983.
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This thesis attempts a synthesis of two important and fast developing branches of mathematics, namely universal algebra and fuzzy set theory. Given an abstract algebra [X,F] where X is a non-empty set and F is a set of finitary operations on X, a fuzzy algebra [I×,F] is constructed by extending operations on X to that on I×, the set of fuzzy subsets of X (I denotes the unit interval), using Zadeh's extension principle. Homomorphisms between fuzzy algebras are defined and discussed. Fuzzy subalgebras of an algebra are defined to be elements of a fuzzy algebra which respect the extended algebra operations under inclusion of fuzzy subsets. The family of fuzzy subalgebras of an algebra is an algebraic closure system in I×. Thus the set of fuzzy subalgebras is a complete lattice. A fuzzy equivalence relation on a set is defined and a partition of such a relation into a class of fuzzy subsets is derived. Using these ideas, fuzzy functions between sets, fuzzy congruence relations, and fuzzy homomorphisms are defined. The kernels of fuzzy homomorphisms are proved to be fuzzy congruence relations, paving the way for the fuzzy isomorphism theorem. Finally, we sketch some ideas on free fuzzy subalgebras and polynomial algebras. In a nutshell, we can say that this thesis treats the central ideas of universal algebras, namely subalgebras, homomorphisms, equivalence and congruence relations, isomorphism theorems and free algebra in the fuzzy set theory setting
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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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Peters, Barry. "Stable fuzzy logic controllers for uncertain dynamic systems." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/18223.
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Melik, Serhat. "Cash Flow Analysis Of Construction Projects Using Fuzzy Set Theory." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612517/index.pdf.
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Construction industry is a one of the most risky sectors due to high level of uncertainties included in the nature of the construction projects. Although there are many reasons, the deficiency of cash is one of the main factors threatening the success of the construction projects and causing business failures. Therefore, an appropriate cash planning technique is necessary for adequate cost control and efficient cash management while considering the risks and uncertainties of the construction projects.
The main objective of this thesis is to develop a realistic, reliable and cost-schedule integrated cash flow modeling technique by using fuzzy set theory for including the uncertainties in project cost and schedule resulting from complex and ambiguous nature of construction works. The linguistic expressions are used for utilizing from human judgment and approximate reasoning ability of users for reflecting their experience into the model to create cash flow scenarios. The uncertain cost and duration estimates gathered from experts are inserted in the model as fuzzy numbers. The model provides the user different net cash flow scenarios with fuzzy formats that are beneficial for foreseeing possible cost and schedule threats to the project during the tender stage. The model is generated in Microsoft Excel 2007 using Visual Basic for applications and the model is applied to a case example.
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Al-Zaidan, Amina S. "Mathematical modeling of marine environment contamination using fuzzy set theory." Thesis, Bangor University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248901.
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Almadani, Firdos Mohammed. "Modelling and analysing vague geographical places using fuzzy set theory." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37352.
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Vagueness is an essential part of how humans perceive and understand the geographical world they occupy. It has now become of increasing important to acknowledge this situation in geographical databases and analyses in the field of Geographical Information Science (GIScience). This research has tackled the wholly original topic of modelling vague geographical places (objects) based on fuzzy set theory with a view to assessing the implications of routing problem around those vague places. The research has focused on the modelling of vague places, for a number of villages and rural settlements, working with national address databases which have numerous ambiguous characteristics which add challenge to the work. It has demonstrated the way in which fuzzy set theory can be used to derive approximate boundaries for vague spatial extents (fuzzy footprint) form sets of precise addresses, reporting rural settlements, recorded in different databases. It has further explored the implications of applying the Travelling Salesman Problem (TSP) in traditional hard village extents versus the modelled fuzzy extents. The introduced methods evaluate the usefulness of fuzzy set theory in modelling and analysing such vague regions. The results imply that the fuzzy model is more efficient than the traditional hard, crisp model of approximating the spatial extent of rural areas. However, the TSP results showed that longer tours were mostly found in the fuzzy model than the traditional crisp model. This is mainly affected by the scale factor of rural areas, considering the relatively small distances between villages. One challenge for the approach outlined here is to incorporate this method applied in other novel analyses of geographical information based on fuzzy representation of geographical phenomena.
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Zettervall, Hang. "Fuzzy Set Theory Applied to Make Medical Prognoses for Cancer Patients." Doctoral thesis, Blekinge Tekniska Högskola [bth.se], Faculty of Engineering - Department of Mathematics and Natural Sciences, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00574.
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As we all know the classical set theory has a deep-rooted influence in the traditional mathematics. According to the two-valued logic, an element can belong to a set or cannot. In the former case, the element’s membership degree will be assigned to one, whereas in the latter case it takes the zero value. With other words, a feeling of imprecision or fuzziness in the two-valued logic does not exist. With the rapid development of science and technology, more and more scientists have gradually come to realize the vital importance of the multi-valued logic. Thus, in 1965, Professor Lotfi A. Zadeh from Berkeley University put forward the concept of a fuzzy set. In less than 60 years, people became more and more familiar with fuzzy set theory. The theory of fuzzy sets has been turned to be a favor applied to many fields. The study aims to apply some classical and extensional methods of fuzzy set theory in life expectancy and treatment prognoses for cancer patients. The research is based on real-life problems encountered in clinical works by physicians. From the introductory items of the fuzzy set theory to the medical applications, a collection of detailed analysis of fuzzy set theory and its extensions are presented in the thesis. Concretely speaking, the Mamdani fuzzy control systems and the Sugeno controller have been applied to predict the survival length of gastric cancer patients. In order to keep the gastric cancer patients, already examined, away from the unnecessary suffering from surgical operation, the fuzzy c-means clustering analysis has been adopted to investigate the possibilities for operation contra to nonoperation. Furthermore, the approach of point set approximation has been adopted to estimate the operation possibilities against to nonoperation for an arbitrary gastric cancer patient. In addition, in the domain of multi-expert decision-making, the probabilistic model, the model of 2-tuple linguistic representations and the hesitant fuzzy linguistic term sets (HFLTS) have been utilized to select the most consensual treatment scheme(s) for two separate prostate cancer patients. The obtained results have supplied the physicians with reliable and helpful information. Therefore, the research work can be seen as the mathematical complements to the physicians’ queries.
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Abbas, Mujahid. "Soft Set Theory: Generalizations, Fixed Point Theorems, and Applications." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/48470.
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Mathematical models have extensively been used in problems related to
engineering, computer sciences, economics, social, natural and medical sciences
etc. It has become very common to use mathematical tools to solve,
study the behavior and different aspects of a system and its different subsystems.
Because of various uncertainties arising in real world situations,
methods of classical mathematics may not be successfully applied to solve
them. Thus, new mathematical theories such as probability theory and fuzzy
set theory have been introduced by mathematicians and computer scientists
to handle the problems associated with the uncertainties of a model. But
there are certain deficiencies pertaining to the parametrization in fuzzy set
theory. Soft set theory aims to provide enough tools in the form of parameters
to deal with the uncertainty in a data and to represent it in a useful
way. The distinguishing attribute of soft set theory is that unlike probability
theory and fuzzy set theory, it does not uphold a precise quantity. This
attribute has facilitated applications in decision making, demand analysis,
forecasting, information sciences, mathematics and other disciplines.
In this thesis we will discuss several algebraic and topological properties
of soft sets and fuzzy soft sets. Since soft sets can be considered as setvalued
maps, the study of fixed point theory for multivalued maps on soft
topological spaces and on other related structures will be also explored.
The contributions of the study carried out in this thesis can be summarized
as follows:
i) Revisit of basic operations in soft set theory and proving some new
results based on these modifications which would certainly set a new
dimension to explore this theory further and would help to extend its
limits further in different directions. Our findings can be applied to
develop and modify the existing literature on soft topological spaces
ii) Defining some new classes of mappings and then proving the existence
and uniqueness of such mappings which can be viewed as a positive
contribution towards an advancement of metric fixed point theory
iii) Initiative of soft fixed point theory in framework of soft metric spaces
and proving the results lying at the intersection of soft set theory and
fixed point theory which would help in establishing a bridge between
these two flourishing areas of research.
iv) This study is also a starting point for the future research in the area of
fuzzy soft fixed point theory.Abbas, M. (2014). Soft Set Theory: Generalizations, Fixed Point Theorems, and Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48470
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Moreau, Aurélien. "How fuzzy set theory can help make database systems more cooperative." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S043/document.
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Dans ces travaux de thèse nous proposons de tirer parti de la théorie des ensembles flous afin d'améliorer les interactions entre les systèmes de bases de données et les utilisateurs. Les mécanismes coopératifs visent à aider les utilisateurs à mieux interagir avec les SGBD. Ces mécanismes doivent faire preuve de robustesse : ils doivent toujours pouvoir proposer des réponses à l'utilisateur. Empty set (0,00 sec) est un exemple typique de réponse qu'il serait désirable d'éradiquer. Le caractère informatif des explications de réponses est parfois plus important que les réponses elles-mêmes : ce peut être le cas avec les réponses vides et pléthoriques par exemple, d'où l'intérêt de mécanismes coopératifs robustes, capables à la fois de contribuer à l'explication ainsi qu'à l'amélioration des résultats. Par ailleurs, l'utilisation de termes de la langue naturelle pour décrire les données permet de garantir l'interprétabilité des explications fournies. Permettre à l'utilisateur d'utiliser des mots de son propre vocabulaire contribue à la personnalisation des explications et améliore l'interprétabilité. Nous proposons de nous intéresser aux explications dans le contexte des réponses coopératives sous trois angles : 1) dans le cas d'un ensemble pléthorique de résultats ; 2) dans le contexte des systèmes de recommandation ; 3) dans le cas d'une recherche à partir d'exemples. Ces axes définissent des approches coopératives où l'intérêt des explications est de permettre à l'utilisateur de comprendre comment sont calculés les résultats proposés dans un effort de transparence. Le caractère informatif des explications apporte une valeur ajoutée aux résultats bruts, et forme une réponse coopérativeIn this thesis, we are interested in how we can leverage fuzzy logic to improve the interactions between relational database systems and humans. Cooperative answering techniques aim to help users harness the potential of DBMSs. These techniques are expected to be robust and always provide answer to users. Empty set (0,00 sec) is a typical example of answer that one may wish to never obtain. The informative nature of explanations is higher than that of actual answers in several cases, e.g. empty answer sets and plethoric answer sets, hence the interest of robust cooperative answering techniques capable of both explaining and improving an answer set. Using terms from natural language to describe data --- with labels from fuzzy vocabularies --- contributes to the interpretability of explanations. Offering to define and refine vocabulary terms increases the personalization experience and improves the interpretability by using the user's own words. We propose to investigate the use of explanations in a cooperative answering setting using three research axes: 1) in the presence of a plethoric set of answers; 2) in the context of recommendations; 3) in the context of a query/answering problem. These axes define cooperative techniques where the interest of explanations is to enable users to understand how results are computed in an effort of transparency. The informativeness of the explanations brings an added value to the direct results, and that in itself represents a cooperative answer
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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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Ornelas, Gilbert. "Set-valued extensions of fuzzy logic classification theorems /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Zettervall, Hang. "Fuzzy and Rough Set Theory in Treatment of Elderly Gastric Cancer Patients." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00489.
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Fuzzy set theory was presented for the first time by Professor Lotfi A. Zadeh from Berkeley University in 1965. In conventional binary logic a statement can be true or false, and there is no place for even a little uncertainty in this judgment. An element either belongs to a set or does not. We call these kinds of sets crisp sets. In practice we often experience those real situations that are represented by crisp sets as impossible to describe accurately. A two-valued logic assumes that precise symbols must be employed, and it is therefore not applicable to the real existence. If the information demanded by a system is lacking, the future state of such a system may not be known completely. One of the instruments used to handle the vagueness in the real-world situations is fuzzy set theory, which has been frequently applied in a wide range of areas like, e.g., dynamic systems, militaries, medicine and other domains. Another theory, which copes with the problem of imprecision, is known as rough set theory. It was proposed by Professor Zdzisław Pawlak in Warsaw in the 1980ties. Whereas imprecision is expressed in the category of a membership degree in fuzzy set theory, this is a matter of the set approximation in rough set theory. Due to the definition of a rough set formulated by means of the decision attribute value, two approximate sets of the rough set are determined. These contain sure and possible members of the universe considered, in which the rough set has been defined. One of the objectives of this study is to apply some classical methods of fuzzy set theory to medicine in order to estimate the survival length of gastric cancer patients. We have decided to test the action of fuzzy controllers of the Mamdani and Sugeno type. Two clinical markers, playing roles of the independent variables, have been included in the algorithm as the base information assisting the survival prognosis. Since the model results have been convergent to the expected experimental values then we will intend to make some extensions of the model concerning the larger number of independent variables. We have also utilized rough set classification, to verify the types of operations. These items are discussed in the thesis in conformity with the physicians’ wishes to support results of statistical investigations. The current research is funded by the scientific grant obtained from Blekinge Research Board.Lic. March 25th
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Abdel-Kader, Magdy G. "Evaluating investment decisions in advanced manufacturing systems : a fuzzy set theory approach." Thesis, University of the West of England, Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362552.
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Lin, Kuo-Sui. "A software quality strategy for the development of automatic control systems." Thesis, University of the West of Scotland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265888.
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Tran, Dat Tat, and n/a. "Fuzzy approaches to speech and peaker recognition." University of Canberra. Management & Technology, 2000. http://erl.canberra.edu.au./public/adt-AUC20061109.151916.
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Stastical pattern recognition is the most successful approach to automatic speech and
speaker recognition (ASASR). Of all the statistical pattern recognition techniques, the hidden
Markov model (HMM) is the most important. The Gaussian mixture model (GMM)
and vector quantisation (VQ) are also effective techniques, especially for speaker recognition
and in conjunction with HMMs. for speech recognition.
However, the performance of these techniques degrades rapidly in the context of insufficient
training data and in the presence of noise or distortion. Fuzzy approaches with their
adjustable parameters can reduce such degradation.
Fuzzy set theory is one of the most, successful approaches in pattern recognition, where,
based on the idea of a fuzzy membership function, fuzzy C'-means (FCM) clustering and
noise clustering (NC) are the most, important techniques.
To establish fuzzy approaches to ASASR, the following basic problems are solved. First,
a time-dependent fuzzy membership function is defined for the HMM. Second, a general
distance is proposed to obtain a relationship between modelling and clustering techniques.
Third, fuzzy entropy (FE) clustering is proposed to relate fuzzy models to statistical models.
Finally, fuzzy membership functions are proposed as discriminant functions in decison
making.
The following models are proposed: 1) the FE-HMM. NC-FE-HMM. FE-GMM. NC-FEGMM.
FE-VQ and NC-FE-VQ in the FE approach. 2) the FCM-HMM. NC-FCM-HMM.
FCM-GMM and NC-FCM-GMM in the FCM approach, and 3) the hard HMM and GMM
as the special models of both FE and FCM approaches. Finally, a fuzzy approach to speaker
verification and a further extension using possibility theory are also proposed.
The evaluation experiments performed on the TI46, ANDOSL and YOHO corpora showbetter
results for all of the proposed techniques in comparison with the non-fuzzy baseline
techniques.
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Haven, Emmanuel. "The use of fuzzy set theory in economics : applications in micro-economics and finance." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23335.
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This paper attempts to show how fuzzy set theory can be used to weaken some of the stringent, rationality assumptions used in classical micro-economics. The objective of the paper is to see whether by introducing fuzziness we arrive to new results or just only generalizations of classical micro-economic results. We discover that the axiom of completeness is not needed anymore. Using fuzziness will also allow us to better explain the existing gap between delimiting possible choices and making the actual choice. We also introduce the notions of a fuzzy indifference set with a measurable area. The fuzzy utility surface is also discussed. The demand curve is now 'thick'.In the producer area, the classical hypothesis that maximum profit entails maximum utility of profit is now substantially weakened when introducing fuzziness.
Finally, we consider revealed preference within a fuzzy context.
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Han, Sedat. "Estimation Of Cost Overrun Risk In Interrnational Project By Using Fuzzy Set Theory." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606032/index.pdf.
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In the global construction market, most construction companies are willing to undertake international projects in order to maximise their profitability by taking advantage of attractive emerging markets and minimise dependence on unfavorable domestic market conditions. In order to be awarded a contract in highly competitive global construction market, companies should excel in choosing the most attractive markets and prepare winning bids for the selected construction projects in those markets. While preparing bids, the major concern of companies is to offer an optimum price that will enable them to earn enough profits and win the contract at the same time, where profit making ability is strongly correlated with proper estimation of a risk premium that is added onto the estimated cost of the project. Due to the nature of construction works, there are lots of uncertainties associated with the project, market and country conditions. Therefore, how the profitability of the project changes with occurrence of various risk events, in other words, the sensitivity of project costs to risk events, should be estimated by bidders realistically. In this study, fuzzy set theory is used to estimate cost overrun risk in international projects at the bidding stage. The objective is to propose a methodology which can be used by bidders to quantify cost overrun risk so that a realistic risk premium may be determined. A fuzzy risk rating approach is proposed to quantify cost overrun risk rating, which takes into account of risks characterised in international construction projects. For this purpose, risk sources have been identified and a risk model is put forward by using influence diagramming method. Based on this risk model, a fuzzy risk rating algorithm has been defined and software has been developed to conduct fuzzy risk rating calculations easily. After a decision-maker inserts the necessary inputs related with project and country risk factors, the output of the software is a rating that takes into account of all factors that may affect cost overrun risk in international construction projects. The reliability of the algorithm and developed software have been tested by an application on a real construction project. The proposed methodology and decision support tool have been proved to be reliable for the estimation of cost overrun risk while giving bidding decisions in international markets.
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Lee, Chang Su. "A framework of adaptive T-S type rough-fuzzy inference systems (ARFIS)." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0192.
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[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinformatics, and so on. Many approaches have been proposed to address the issue of automatic generation of membership functions and rules with the corresponding subsequent adjustment of them towards more satisfactory system performance. Because one of the most important factors for building high quality of FIS is the generation of the knowledge base of it, which consists of membership functions, fuzzy rules, fuzzy logic operators and other components for fuzzy calculations. The design of FIS comes from either the experience of human experts in the corresponding field of research or input and output data observations collected from operations of systems. Therefore, it is crucial to generate high quality FIS from a highly reliable design scheme to model the desired system process best. Furthermore, due to a lack of a learning property of fuzzy systems themselves most of the suggested schemes incorporate hybridization techniques towards better performance within a fuzzy system framework. ... This systematic enhancement is required to update the FIS in order to produce flexible and robust fuzzy systems for unexpected unknown inputs from real-world environments. This thesis proposes a general framework of Adaptive T-S (Takagi-Sugeno) type Rough-Fuzzy Inference Systems (ARFIS) for a variety of practical applications in order to resolve the problems mentioned above in the context of a Rough-Fuzzy hybridization scheme. Rough set theory is employed to effectively reduce the number of attributes that pertain to input variables and obtain a minimal set of decision rules based on input and output data sets. The generated rules are examined by checking their validity to use them as T-S type fuzzy rules. Using its excellent advantages in modeling non-linear systems, the T-S type fuzzy model is chosen to perform the fuzzy inference process. A T-S type fuzzy inference system is constructed by an automatic generation of membership functions and rules by the Fuzzy C-Means (FCM) clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least-squares method and a conjugate gradient descent algorithm towards better performance within a fuzzy system framework. To show the viability of the proposed framework of ARFIS, the performance of ARFIS is compared with other existing approaches in a variety of practical applications; pattern classification, face recognition, and mobile robot navigation. The results are very satisfactory and competitive, and suggest the ARFIS is a suitable new framework for fuzzy inference systems by showing a better system performance with less number of attributes and rules in each application.
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Wilson, Duncan John. "Classification of defects using uncertainty in industrial web inspection." Thesis, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286894.
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Saeed, Mehreen. "Soft AI methods and visual speech recognition." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299270.
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Yeung, Fai Yip. "Developing a Partnering Performance Index (PPI) for construction projects : a fuzzy set theory approach." online access from Digital Dissertation Consortium, 2007. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3299884.
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Ralbovský, Martin. "Fuzzy GUHA." Doctoral thesis, Vysoká škola ekonomická v Praze, 2006. http://www.nusl.cz/ntk/nusl-77047.
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The GUHA method is one of the oldest methods of exploratory data analysis, which is regarded as part of the data mining or knowledge discovery in databases (KDD) scienti_c area. Unlike many other methods of data mining, the GUHA method has firm theoretical foundations in logic and statistics. In scope of the method, finding interesting knowledge corresponds to finding special formulas in satisfactory rich logical calculus, which is called observational calculus. The main topic of the thesis is application of the "fuzzy paradigm" to the GUHA method By the term "fuzzy paradigm" we mean approaches that use many-valued membership degrees or truth values, namely fuzzy set theory and fuzzy logic. The thesis does not aim to cover all the aspects of this application, it emphasises mainly on: - Association rules as the most prevalent type of formulas mined by the GUHA method - Usage of fuzzy data - Logical aspects of fuzzy association rules mining - Comparison of the GUHA theory to the mainstream fuzzy association rules - Implementation of the theory using the bit string approach The thesis throughoutly elaborates the theory of fuzzy association rules, both using the theoretical apparatus of fuzzy set theory and fuzzy logic. Fuzzy set theory is used mainly to compare the GUHA method to existing mainstream approaches to formalize fuzzy association rules, which were studied in detail. Fuzzy logic is used to define novel class of logical calculi called logical calculi of fuzzy association rules (LCFAR) for logical representation of fuzzy association rules. The problem of existence of deduction rules in LCFAR is dealt in depth. Suitable part of the proposed theory is implemented in the Ferda system using the bit string approach. In the approach, characteristics of examined objects are represented as strings of bits, which in the crisp case enables efficient computation. In order to maintain this feature also in the fuzzy case, a profound low level testing of data structures and algoritms for fuzzy bit strings have been carried out as a part of the thesis.
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Hood, William School of Information Library & Archive Studies UNSW. "An informetric study of the distribution of bibliographic records in online databases: a case study using the literature of Fuzzy Set Theory (1965-1993)." Awarded by:University of New South Wales. School of Information Library and Archive Studies, 1999. http://handle.unsw.edu.au/1959.4/18184.
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This study investigated the distribution of bibliographic records amongst online bibliographic databases. The topic of Fuzzy Set Theory over the period of 1965 to 1993 was chosen to provide the case study for this investigation. From the DIALOG database host, searches were conducted on 114 databases to determine the number of journal article records relating to the topic of Fuzzy Sets. Both the number of records in each database, as well as the overlap of coverage between the databases were calculated. Six counting techniques were developed to allocate records to databases based on different methods for handling records that were duplicated between databases. When duplicate records are included, the top database accounts for 19% of the records; when duplicates are removed, the top database was found to account for 37% of the records. The distribution of records in databases was found to conform to the Bradford-Zipf hyperbolic distribution. Various other analyses were undertaken including: the duplicate records themselves, the total size of the DIALOG database system over time and the density of Fuzzy Set records in databases over time. A secondary aim of this study was to perform an informetric study on the literature of Fuzzy Set Theory itself. Results obtained include an analysis of the growth of the Fuzzy Set literature, an analysis of the journals covering the topic of Fuzzy Sets, an analysis of the terminology used in describing topics related to Fuzzy Sets. Also, the Ulrich's database was used to provide a subject classification of the journals to analyse the diffusion of the topic of Fuzzy Sets into other disciplines. Apart from the discipline of mathematics, the top disciplines into which Fuzzy Sets have diffused were found to be applied physics, systems and computing. The third aim of the thesis was to refine and develop the methodology used to perform large scale informetric studies using data from a variety of online bibliographic databases. Commercially available software was used wherever possible, but where this was not possible or infeasible, custom written programs were developed to perform various steps in the methodology.
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Butler, Chandre. "THE DEVELOPMENT OF A FUZZY MODEL TO QUANTIFY TRAINING ANDEDUCATIONAL FACTORS AND THE RESULTING IMPACT ON STUDENT SUCCESSAND LEAR." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3638.
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The utilization of fuzzy mathematical modeling for quantification of the quality of training and educational delivery is an innovative application that can result in measurable and repeatable results. This research was designed to apply proven quantification techniques and Industrial Engineering methodologies to a nontraditional environment. The outcomes of this research provide the foundation, initial steps and preliminary validation for the development of a systematic fuzzy theoretical model to be applied for the quantification of various areas within training and education delivery. The test bed for this methodology is Orange County Public School system, the twelfth largest school district in the nation. The organizational and operational factors of a large school district are highly compatible with Systems Engineering concepts. The debate over education reform has drawn from referenced areas within the Industrial Engineering community including quality, continuous improvement, benchmarking and metrics development, data analysis, and scientific/systemic justification requirements. In spite of these applications, the literature does not reflect a consistent and broad application of these techniques in addressing the evaluation and quantification of educational delivery systems. This research draws on the previously listed areas within Industrial Engineering to apply these techniques to enhance the understanding and promote quantification of the multiple factors acting on the educational delivery system. The importance of addressing these issues is a national concern given the significant changes in the United States educational delivery system. For example, over the past 40 years there has been a more than three-hundred percent increase in per-pupil appropriations yet the academic performance gains have been limited and the quantification and measurement of those gains is even more limited. This body of work willidentify the systems, sub-systems, system factors, and factor degrees of existence necessary to quantify and measure these performance changes. Finally, the research will quantify the inputs and produce a model that provides a numeric value that represents the condition of the system and various subsystems of an educational system.M.S.I.E.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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Menacer, Mohamed. "A contribution to the automation of DNA fingerprint analysis." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318646.
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Hill, Carla. "Mass assignments for inductive logic programming." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325748.
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Piggins, Ashley James. "Essays on quasi-orderings and population ethics." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324363.
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Bicker, Marcelle M. "A toolkit for uncertainty reasoning and representation using fuzzy set theory in PROLOG expert systems /." Online version of thesis, 1987. http://hdl.handle.net/1850/10294.
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Kabnurkar, Amit. "Mathematical Modeling for Data Envelopment Analysis with Fuzzy Restrictions on Weights." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/31992.
Full textAbstract:
Data envelopment analysis (DEA) is a relative technical efficiency measurement tool, which uses operations research techniques to automatically calculate the weights assigned to the inputs and outputs of the production units being assessed. The actual input/output data values are then multiplied with the calculated weights to determine the efficiency scores. Recent variants of the DEA model impose upper and lower bounds on the weights to eliminate certain drawbacks associated with unrestricted weights. These variants are called weight restriction DEA models. Most weight restriction DEA models suffer from a drawback that the weight bound values are uncertain because they are determined based on either incomplete information or the subjective opinion of the decision-makers. Since the efficiency scores calculated by the DEA model are sensitive to the values of the bounds, the uncertainty of the bounds gets passed onto the efficiency scores. The uncertainty in the efficiency scores becomes unacceptable when we consider the fact that the DEA results are used for making important decisions like allocating funds and taking action against inefficient units.
In order to minimize the effect of the uncertainty in bound values on the decision-making process, we propose to explicitly incorporate the uncertainty in the modeling process using the concepts of fuzzy set theory. Modeling the imprecision involves replacing the bound values by fuzzy numbers because fuzzy numbers can capture the intuitive conception of approximate numbers very well. Amongst the numerous types of weight restriction DEA models developed in the research, two are more commonly used in real-life applications compared to the others. Therefore, in this research, we focus on these two types of models for modeling the uncertainty in bound values. These are the absolute weight restriction DEA models and the Assurance Region (AR) DEA models.
After developing the fuzzy models, we provide implementation roadmaps for illustrating the development and solution methodology of those models. We apply the fuzzy weight restriction models to the same data sets as those used by the corresponding crisp weight restriction models in the literature and compare the results using the two-sample paired t-test for means. We also use the fuzzy AR model developed in the research to measure the performance of a newspaper preprint insertion line.Master of Science
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GUO, HAN-JING, and 郭寒菁. "Contingency selection using fuzzy set theory." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/03722877472614246612.
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Jan-Yang, Chen. "convexity with applications to fuzzy set theory." 2000. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611364432.
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Chen, Jan-Yang, and 陳振陽. "convexity with applications to fuzzy set theory." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/07132983064566871041.
Full textAbstract:
碩士元智大學
工業工程研究所
88
The concept of -convexity and -quasiconvexity for fuzzy sets and fuzzy mappings are introduced and studied. Characterization theorems for these fuzzy sets and fuzzy mappings are established. In addition, the concept of -convex and -quasiconvex fuzzy processes are proposed. Some basic properties for these fuzzy processes are presented.
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Ko, Fu-Jen, and 柯福建. "Application of Fuzzy set Theory on Instrument Measurement." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/48504951447191147778.
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Chang, Lin Jing, and 林敬章. "Applicability of Fuzzy Set Theory in Rainfall Classification." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/29590239638771995409.
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Yuan-Ming, Fan, and 范原鳴. "Fuzzy set theory applied to robot color matching." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/86357222249490247432.
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碩士國立高雄應用科技大學
工業工程與管理系碩士在職專班
103
Rapid developments of high technology and declining birth rates in the worldwide have an influence that intelligent robots have been accelerated to become a part of family life. In order to raise customers’ acceptability of robots, the discussions which only focus on functionality and practicability has been unable to meet market requirements. It becomes more necessary to discuss and to analyze it in a emotional perspective such as shape, texture and color in appearance. It would be beneficial to do market segmentation and to focus on target markets if customers’ emotional requirements can be caught and be used for design and planning. In this research, the emotional requirement “Color” and “adjectives” are two main variables. Fuzzy theory is used for analyzing the corresponding relations between these two variables to create a database of color-decision-making for domestic robots. This research is mainly separated into 4 steps. It includes collecting and screening out vocabulary and hue which can describe domestic robot, applying them into a domestic robot case study and researching fuzzy subordination to build fuzzy matrix of “color” and “adjectives” and to analyze the corresponding relations between adjectives and color among different groups of participants. Finally, cluster analysis is used for categorization and induction of color on the basis of color evaluation results of different groups. Case study and application show that color-decision-making system created by this research is beneficial for designers to refer to and to use when conducting color planning. It makes customers’ abstract emotional imaginary become more objective and precise through data from the color database; moreover, it improves design efficiency and transparency in process.
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Campbell, Paul Francis. "Applications of fuzzy set theory in reservoir operations." Thesis, 1993. http://hdl.handle.net/2429/4914.
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Attempts to maximize benefits from hydroelectric reservoirs with
mathematical models have been a focus of study for water resource specialists.
As increasingly complex models are developed to more closely imitate reality,
their usefulness may paradoxically diminish since reservoir operators are less
apt to fully understand the models. This results in a general lack of acceptance
of mathematical reservoir models amongst the people they were originally
developed to serve. Also, the stochastic nature of modelling a system
influenced by climatic and economic factors such as a hydroelectric reservoir
puts an upper limit on the attainable accuracy of a model. This thesis suggests
that a method based on fuzzy set theory may provide a more readily
understandable model that recognizes the inherent uncertainties in reservoir
modelling. Heuristics or “rules of thumb” are used to simulate operation of a
reservoir subject to uncertain inflows and changing hydroelectric power values.
This system describes the operation of the reservoir in vague terms such as: “IF
predicted inflow is medium-low AND reservoir volume is high THEN suggested
outflow is ...“. These rules can be obtained directly from an experienced
operator; from analysis of historical data; from data generated by a
mathematical simulation of the reservoir or any combination of all three.
This thesis illustrates the development and use of a simple fuzzy rulebase
for a single hydroelectric reservoir. The rulebase is formed from data generated
by a mathematical optimization model (dynamic programming) of the reservoir
that simulates several years of operation with random inputs.
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Ho, Hsin-Ni, and 何昕霓. "Topic Identification System Based on Fuzzy Set Theory." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/61638910311497182311.
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碩士國立臺灣大學
機械工程學研究所
90
A topic identification system based on the fuzzy set theory is proposed to handle the inherent fuzziness in the human language. This system is self-trained by the training corpus, membership function, and relevance function to construct the characteristic description of each destination and to define the topic identification threshold value. The process of identifying user query’s topic is to calculate the user query’s relevance scores for all destinations. If the largest relevance score is higher than the topic identification threshold value, the user query’s topic will belong to the corresponding destination of the largest relevance score. If the largest relevance score is smaller than the topic identification threshold value, the system will transfer this user query to the operator for further service. Based on the traditional term frequency and inverse document frequency indexing scheme, this paper proposes various types of membership functions to construct four types of topic identification systems. The four topic identification systems are trained by the training corpus, tested by the testing corpus obtained from questionnaires, and evaluated for their performance. The best efficiency of the system with the exponent of the concentration coefficient to be 1 is 70.2%, and it is not much less than the efficiency of 75.6% for the existing topic identification systems VBNLCR and HMIHY trained by the larger training corpus. It indicates that the topic identification system proposed in this study could obtain similar efficiency of the system as existing systems with the less training corpus.
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Hsieh, Hai-Teh, and 謝海德. "Applications of Fuzzy Set Theory in Bank Credit Policy." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/60981511985628952887.
Full textAbstract:
碩士元智大學
工業工程研究所
87
Making loans by commercial banks to firms is an important behavior in financial economy. However, when a bank processes a loan business, it also carries some credit risks. Therefore, assessing the loaner’s credit is critical. Traditionally, a bank evaluates a customer’s credit according to “The credit rating standard for lending to enterprises” published by Committee of the Bank of Taipei City. This standard can be divided into the following three categories: 1.Financial condition of the enterprise, 2.Management, and 3.The particular characteristics of the main product, competition and expectation. In the first category, financial ratios are calculated to evaluate the financial condition of an enterprise and each ratio is represented by five different variables, from 1 to 5. This is due to the problems of anticatastrophism and does not satisfy with the property of sensitivity. Thus, instead of using the five variables, we make the crisp scores into corresponding normal fuzzy numbers, with trapezoidal (or triangular) membership functions. By applying the concept of mean and fuzzy set theory, we proposed a reasonable algorithm to evaluate enterprises financial conditions. No numerical numbers can be obtained for the other two categories and thus only a linguistic representation can be used. It is proven that a fuzzy set theory can handle the linguistic representation, which is generally used in describing the various parameters, effectively. Hence, we convert linguistic terms to fuzzy numbers by Chen&Hwang conversion scale. And then aggregate these fuzzy numbers, and evaluate the value of the fuzzy numbers, which is proposed by Delgado, Vila, and Voxmen, in order to represent the rating of the two categories.
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Cheng, Ya-Yun, and 鄭雅云. "Applications of Fuzzy Set Theory in Supply Chain Management." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/01064259482373604237.
Full textAbstract:
碩士元智大學
工業工程研究所
89
The study offers a fuzzy linear supply chain model that operates in an uncertain environment. It shows that the external supplier reliability and the external consumer demand are the major factors of the uncertainty. This study uses fuzzy sets to explain the uncertainty of linear supply chain, and its major goal is to determine the order quantities for each inventory in the supply chain, that give an acceptable service level of the supply chain at reasonable total cost (to minimize both holding cost and shortage cost in each stock period). In addition, two control concepts of the supply chain are treated: (1) fully decentralized control of each inventory and (2) partial coordinate control in the inventories. These two methods can individually turn out the best fuzzy decision and evaluate the supply chain performance as well. The results of this study are as follows. (1)The best order quantities of the fuzzy linear supply chain model using fuzzy sets to explain the uncertainty is a fuzzy decision. It shows that the best order quantities will be relatively changed according to different possibilities of goods need from the possibilities of the external supplier reliability and the external consumer demand. (2) When the possibilities of the external supplier reliability and the external consumer demand become increasing, the best order quantities turns out more clearly. On the other hand, when these two possibilities mentioned above become decreasing, the best order quantities turns out more fuzzily. (3) By using fully decentralized control to plan order quantities and inventories in each inventory of the linear supply chain, it reflects that the best order quantities in each inventory turns out the same. (4) By using partial coordinate control to plan order quantities and inventories in each inventory of the linear supply chain, it reflects that the difference compared with fully decentralized control is that the best order quantities in each inventory turns out differently. (5) When the possibilities of the external supplier reliability and the external consumer demand become increasing, the fill rate of the supply chain is also relatively increasing. On the other hand, when the possibilities mentioned above become decreasing, the fill rate of the supply chain is decreasing too. (6) Partial coordinate control can result a better fill rate of the supply chain. Thus, in case consumer’s satisfaction serves as the sole factor for decision making, then the method of partial coordinate control is recommended to use so as to plan order quantities and inventories in each inventory of the linear supply chain. (7) Fully decentralized control can result a better control on the cost of the supply chain. Thus, in case cost control serves as the sole factor for decision making, then the method of fully decentralized control is recommended to use so as to plan order quantities and inventories in each inventory of the linear supply chain.
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Chuang, Tzung-Nan, and 莊宗南. "Application of Fuzzy Set Theory to Flow Shop Scheduling." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/96993382111566312499.
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碩士高雄工學院
管理科學系
85
Scheduling is an important process widely used in manufacturing, production, management, computer science, and so on. It mainly consists of allocating resources to jobs over time under necessary constraints. Appropriate scheduling reduces throughput time and raises the product quality. One kind of scheduling problems that frequently occurs in real-word application environments is the flow shop problems. In a flow shop, jobs are processed by a series of machines in a predefined order. In the past, the processing time for each job was usually assigned or estimated as a fixed value. In many real-world applications, however, job processing time may vary dynamically with the situation. If the time required to process each job is uncertain, then the finish time of a scheduling schema is apparently also uncertain. For a system manager to have a broader overall view of scheduling, scheduling results for all foreseeable situations must be reported and analyzed. In this thesis, discrete and triangular fuzzy set concepts are used in four scheduling algorithms including Johnson, Palmer, Gupta and CDS algorithms to schedule jobs with uncertain time for flow shop problems. Also, the conventional scheduling algorithms are shown as a special case of the fuzzy scheduling algorithms with special membership functions being assigned. The fuzzy scheduling algorithms are then a feasible solution for both deterministic and uncertain flow shop problems.
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Huang, Kuo-Liang, and 黃國亮. "The Application of Fuzzy Set Theory in Cluster Analysis." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/15430180225929429017.
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Wang, J. K., and 王京國. "Application Of Fuzzy Set Theory In Rational Slopeland Development." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/87416092032743367902.
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Ling, Yi-Ching, and 林宜慶. "Applying Fuzzy Set Theory to Assess the Learning Degree." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/67257871400010367580.
Full textAbstract:
碩士大葉大學
資訊管理學系碩士在職專班
93
It is important to consider the difference among students in the process of teaching and learning. In general, the educational tests are usually used to assess the learning performance of each student. For this viewpoint, a suitable assessment method will help the performance evaluation of each student. Using the traditional grading methods to assign grades are of the most common ways of educational evaluation. However, these methods are difficult to assess the learning degree of each student because the subjective judgments of teachers are fuzziness in the evaluation process. Therefore, a new is proposed in this study method to evaluate the learning degree of each student by using 2-tuple linguistic variables. The proposed method not only to assess the learning degree of each student, but also to help teachers ranking the order of student in accordance with final the linguistic ratings.In a word, the proposed method can avoid the complicated operations and it can assess the learning degree of each student in a more objective manner.
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Chang, YuanPo, and 張淵博. "Application of Fuzzy Set Theory in Actuary —Using Fuzzy Number as an Example." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/38054738551328005371.
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碩士國立高雄第一科技大學
保險營運系
88
This article describes how fuzzy number can be used to distinguish the little difference between every individual insured, which can let insurance pricing be more fairness. Via the method of fuzzification to the interest and mortality, we define a new net premium as form of fuzzy number. Using whole life, term life, endowment, pure endowment and deferred annuity as an example, we conclude that fuzzy number provides an effective method in actuary. Besides, we can optimize profit by controlling alpha-cut.
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"Portfolio selection based on minmax rule and fuzzy set theory." 2011. http://library.cuhk.edu.hk/record=b5894705.
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Yang, Fan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 100-106).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Literature review --- p.1
Chapter 1.2 --- The main contribution of this thesis --- p.5
Chapter 1.3 --- Relations between the above three models --- p.7
Chapter 2 --- Model 1 --- p.9
Chapter 2.1 --- Introduction --- p.9
Chapter 2.2 --- Minimax rule risk function --- p.11
Chapter 2.3 --- Fuzzy liquidity of asset --- p.12
Chapter 2.4 --- Notations --- p.15
Chapter 2.5 --- Model formulation --- p.16
Chapter 2.6 --- Numerical example and result --- p.25
Chapter 3 --- Model 2 --- p.36
Chapter 3.1 --- Introduction --- p.36
Chapter 3.2 --- Notations --- p.39
Chapter 3.3 --- Model formulation --- p.41
Chapter 3.4 --- Numerical example and result --- p.45
Chapter 4 --- Model 3 --- p.51
Chapter 4.1 --- Introduction --- p.51
Chapter 4.2 --- Notations --- p.52
Chapter 4.3 --- Model formulation --- p.54
Chapter 4.4 --- Numerical example and result --- p.62
Chapter 5 --- Conclusion --- p.68
Chapter A --- Source Data for Model 1 --- p.71
Chapter B --- Source Data for Model 2 --- p.80
Chapter C --- Source Data for Model 3 --- p.90
Bibliography --- p.100
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Hui-Yu, Hwang. "COLOR PERCEPTION OF A NATURAL IMAGE BY FUZZY SET THEORY." 1993. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611355196.
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