Thèses sur le sujet « Fuzzy Methods »
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1
Matthews, Chris, et mikewood@deakin edu au. « Fuzzy concepts and formal methods ». Deakin University. School of Management Information Systems, 2001. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051201.154843.
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It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.
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Swartz, Andre Michael. « Methods for designing and optimizing fuzzy controllers ». Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1005226.
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We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.
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Prajitno, Prawito. « Neuro-fuzzy methods in multisensor data fusion ». Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.
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FURUHASHI, Takeshi, et Makoto YASUDA. « Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods ». Institute of Electronics, Information and Communication Engineers, 2009. http://hdl.handle.net/2237/15060.
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Wang, Yanfei. « Fuzzy methods for analysis of microarrays and networks ». Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48175/1/Yanfei_Wang_Thesis.pdf.
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Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.
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Abd, Rahim Noor Hafhizah. « Comparing and compressing fuzzy concepts : methods and application ». Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682484.
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In recent years, the volume of data has risen so rapidly due to the Internet and World Wide Web development. This phenomenon called information overload or digital obesity has caused data explosion and may lead to storage problems in the future. Many forms of data are stored and transmitted via internet including textual data. Textual data, which is usually in unstructured form can be processed or mined to yield useful information. In order to represent that, we need to know the underlying concepts. The most suitable approach to model the concepts is to design an ontology. Formal Concept Analysis (FCA) is complementary to the ontology approach, and provides a hierarchical structure of the concepts. However, an ontology is a fixed structure which does not change; in contrast, data is typically updated from day to day. The focus of this research is quantifying the changes in the content and structure of these concept hierarchies. it is beneficial if we quantify the changes. There are two types of measurements. The first measures the changes between two lattices which have identical sets of objects, but disjoint sets of attributes. We pair the overlapped concepts and compute the cost to transform each concept to its counterpart. We adapt the Levenstein distance to measure the changes. The second is Support-based Distance measurement, where we quantify the change in two lattices which have different sets of objects but the same set of attributes. We compute the support (or relative cardinality) for each concept's extension. Nowadays, online shopping becomes more common, and many customers, retailers, and manufacturers give attention to the product reviews. Because of that, we apply both measurements to an illustrative application using product review datasets. We monitor the differences between positive and negative sentiment orientations based on a product over fixed period of time using Edit Distance measurement. Additionally, we track the changes between lattices which represent the sentiment orientation on a product in two different time periods using Support-based Distance measurement. The phenomenon of information overload leads to problems using FCA, as it can be difficult to read the lattices and very costly to compute them. These large datasets are often high-dimensional datasets. We enhance an approach to select the important dimensions using Principal Component Analysis (PCA) through the Singular Value Decomposition (SVD) method, so that FCA computation becomes more tractable.
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Siddique, Muhammad. « Fuzzy decision making using max-min and MMR methods ». Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3042.
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Fuzzy logic is based on the theory of fuzzy sets, where an object’s membership of a set is gradual rather than just member or not a member. Fuzzy logic uses the whole interval of real numbers between zero (False) and one (True) to develop a logic as a basis for rules of inference. Particularly the fuzzified version of the modus ponens rule of inference enables computers to make decisions using fuzzy reasoning rather than exact. We study decision making problem under uncertainty. we analyze Max-Min method and Minimization of regret method originally developed by Savage and further developed by Yager. We generalize The MMR method by creating the parameterized family of minimum regret methods by using the ordered weighted averaging OWA operators.
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Nasser, Sara. « Fuzzy methods for meta-genome sequence classification and assembly ». abstract and full text PDF (free order & ; download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3307706.
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Eliason, Ryan Lee. « Application of Convex Methods to Identification of Fuzzy Subpopulations ». BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2242.
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In large observational studies, data are often highly multivariate with many discrete and continuous variables measured on each observational unit. One often derives subpopulations to facilitate analysis. Traditional approaches suggest modeling such subpopulations with a compilation of interaction effects. However, when many interaction effects define each subpopulation, it becomes easier to model membership in a subpopulation rather than numerous interactions. In many cases, subjects are not complete members of a subpopulation but rather partial members of multiple subpopulations. Grade of Membership scores preserve the integrity of this partial membership. By generalizing an analytic chemistry concept related to chromatography-mass spectrometry, we obtain a method that can identify latent subpopulations and corresponding Grade of Membership scores for each observational unit.
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Kotta, Anwesh. « Condition Monitoring : Using Computational intelligence methods ». Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187100.
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Machine tool components are widely used in many industrial applications. In accordance with their usage, a reliable health monitoring system is necessary to detect defects in these components in order monitor machinery performance and avoid malfunction. Even though several techniques have been reported for fault detection and diagnosis, it is a challenging task to implement a condition monitoring system in real world applications due to their complexity in structure and noisy operating environment. The primary objective of this thesis is to develop novel intelligent algorithms for a reliable fault diagnosis of machine tool components. Another objective is to use Micro Electro Mechanical System (MEMS) sensor and interface it with Raspberry pi hardware for the real time condition monitoring.
Primarily knowledge based approach with morphological operators and Fuzzy Inference System is proposed, the e˙ectiveness of this approach lies in the selection of structuring elements(SEs). When this is evaluated with di˙erent classes of bearing fault signals, it is able to detect the fault frequencies e˙ectively. Secondarily, An analytical approach with multi class support machine is proposed, this method has uniqueness of learning on its own with out any prior knowledge, the e˙ectiveness of this method lies on selected features and used kernel for converging. Results have shown that RBF (Radial Bias Function) kernel, which is commonly known as gauss kernel has good performance in identifying faults with less computation time. An idea of prototyping these methods has triggered in using Micro Electro Mechanical System (MEMS) sensor for data acquisition and real time Condition Monitoring. LIS3DH accelerometer sensor is used for the data acquisition of spindle for capturing high frequency fault signals. The measured data is analyzed and compared with the industrial sensor k-shear accelerometer type 8792A.
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Felizardo, Rui Miguel Meireles. « A study on parallel versus sequential relational fuzzy clustering methods ». Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5663.
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Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaRelational Fuzzy Clustering is a recent growing area of study. New algorithms have been developed,as FastMap Fuzzy c-Means (FMFCM) and the Fuzzy Additive Spectral Clustering Method(FADDIS), for which it had been obtained interesting experimental results in the corresponding founding works. Since these algorithms are new in the context of the Fuzzy Relational clustering community, not many experimental studies are available. This thesis comes in response to the need of further investigation on these algorithms, concerning a comparative experimental study from the two families of algorithms: the parallel and the sequential versions. These two families of algorithms differ in the way they cluster data. Parallel versions extract clusters simultaneously from data and need the number of clusters as an input parameter of the algorithms, while the sequential versions extract clusters one-by-one until a stop condition is verified, being the number of clusters a natural output of the algorithm. The algorithms are studied in their effectiveness on retrieving good cluster structures by analysing the quality of the partitions as well as the determination of the number of clusters by applying several validation measures. An extensive simulation study has been conducted over two data generators specifically constructed for the algorithms under study, in particular to study their robustness for data with noise. Results with benchmark real data are also discussed. Particular attention is made on the most adequate pre-processing on relational data, in particular on the pseudo-inverse Laplacian transformation.
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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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Závěrka, Pavel. « Možnosti využití neurčité logiky v oceňovací praxi ». Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-232474.
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Abstract The following thesis discusses the problems of apprising methods of real estate with regard to subjective factor which is inherited in the process by the appricing subject. It discusses methods, evaluations and points out possible disturbing effects and faults which could influence these methods. The example case study shows possibilities in using the power of fuzzy logic, which contributes in a significant way to higher transparency, reproducibility and portability of the whole appricing process. The main goal of the thesis is to introduce the advantages and power of a new evaluation method in the appricing process.
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Walz, Nico-Philipp [Verfasser]. « Fuzzy Arithmetical Methods for Possibilistic Uncertainty Analysis / Nico-Philipp Walz ». Aachen : Shaker, 2016. http://d-nb.info/1124366709/34.
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McNamara, Simon Richard. « Twistor inspired methods in perturbative field theory and fuzzy funnels ». Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440454.
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Styliandidis, Orestis. « Knowledge from data : concept induction using fuzzy and neural methods ». Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361076.
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CHEN, SHANGYE. « ENHANCING FUZZY CLUSTERING METHODS FOR IMAGE SEGMENTATION USING SPATIAL INFORMATION ». Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1556555486273.
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Lee, Stephanie Scheibe. « Fuzzy Membership Function Initial Values : Comparing Initialization Methods That Expedite Convergence ». VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd/852.
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Fuzzy attributes are used to quantify imprecise data that model real world objects. To effectively use fuzzy attributes, a fuzzy membership function must be defined to provide the boundaries for the fuzzy data. The initialization of these membership function values should allow the data to converge to a stable membership value in the shortest time possible. The paper compares three initialization methods, Random, Midpoint and Random Proportional, to determine which method optimizes convergence. The comparison experiments suggest the use of the Random Proportional method.
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Kwan, Alvin Chi Ming. « A framework for mapping constraint satisfaction problems to solution methods ». Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339436.
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McCulloch, Josie C. « Novel methods of measuring the similarity and distance between complex fuzzy sets ». Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33401/.
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This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare. This thesis presents methods of comparing data that may be represented by such (complex) non-normal or non-convex fuzzy sets. The developed approaches for calculating relative comparisons also enable fusing methods of measuring similarity and distance between fuzzy sets. By using multiple methods, more meaningful comparisons of fuzzy sets are possible. Whereas if only a single type of measure is used, ambiguous results are more likely to occur. This thesis provides a series of advances around the measuring of similarity and distance. Based on them, novel applications are possible, such as personalised and crowd-driven product recommendations. To demonstrate the value of the proposed methods, a recommendation system is developed that enables a person to describe their desired product in relation to one or more other known products. Relative comparisons are then used to find and recommend something that matches a person's subjective preferences. Demonstrations illustrate that the proposed method is useful for comparing complex, non-normal and non-convex fuzzy sets. In addition, the recommendation system is effective at using this approach to find products that match a given query.
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Ward, James L. « A Comparison of Fuzzy Logic Spatial Relationship Methods for Human Robot Interaction ». NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-12172008-125840/.
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As the science of robotics advances, robots are interacting with people more frequently. Robots are appearing in our houses and places of work acting as assistants in many capacities. One aspect of this interaction is determining spatial relationships between objects. People and robots simply can not communicate effectively without references to the physical world and how those objects relate to each other. In this research fuzzy logic is used to help determine the spatial relationships between objects as fuzzy logic lends itself to the inherent imprecision of spatial relationships. Objects are rarely absolutely in front of or to the right of another, especially when dealing with multiple objects. This research compares three methods of fuzzy logic, the angle aggregation method, the centroid method and the histogram of angles â composition method. First we use a robot to gather real world data on the geometries between objects, and then we adapt the fuzzy logic techniques for the geometry between objects from the robot's perspective which is then used on the generated robot data. Last we perform an in depth analysis comparing the three techniques with the human survey data to determine which may predict spatial relationships most accurately under these conditions as a human would. Previous research mainly focused on determining spatial relationships from an allocentric, or bird's eye view, where here we apply some of the same techniques to determine spatial relationships from an egocentric, or observer's point of view.
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FILHO, ANTONIO CARLOS DE SOUZA SAMPAIO. « MODIFIED CAPITAL BUDGETING METHODS UNDER UNCERTAINTIES : AN APPROACH BASED ON FUZZY NUMBERS ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37098@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROEssa tese apresenta uma abordagem alternativa para orçamento de capital, denominada Métodos Modificados de Avaliação de Projetos de Investimentos em Ambiente Fuzzy, para avaliação de projetos em condições de incerteza. O desenvolvimento da abordagem proposta está dividido em duas fases: na primeira fase, é estabelecido um modelo determinístico generalizado que prevê explicitamente a utilização dos custos de oportunidade associados com os fluxos de caixa intermediários de um projeto de investimento empresarial. Os pressupostos implícitos dos métodos modificados da taxa interna de retorno e do valor presente líquido são incluídos nos métodos do índice de lucratividade e do tempo de retorno do investimento total. Os indicadores resultantes são o índice de lucratividade modificado e o tempo de retorno do investimento modificado. Essa abordagem unificada tem a propriedade de coincidir as decisões de aceitação / rejeição de projetos de investimentos de mesmos horizontes de vida e escalas com as do valor presente líquido modificado e, portanto, maximizam a riqueza do acionista. Na segunda fase, números fuzzy triangulares são utilizados para representar as incertezas das variáveis de um projeto de investimento: os fluxos de caixa, as taxas de financiamento e de reinvestimento e a taxa de desconto ajustada ao risco. Os indicadores fuzzy resultantes são o valor presente líquido modificado, a taxa interna de retorno modificada, o índice de lucratividade modificado e o tempo de retorno do investimento modificado. A aplicação de custos de oportunidades e de critérios difusos para a atribuição dos valores das variáveis permite obter resultados mais realistas e compatíveis com as condições de mercado. Devido à complexidade dos cálculos envolvidos, novas funções financeiras de uso amigável são desenvolvidas utilizando Visual Basic for Applications do MS-Excel: três, para avaliação de projetos em condições de certeza (MVPL, MIL e MTRI) e quatro para avaliação em condições de incerteza (MVPLfuzzy, MTIRfuzzy, MILfuzzy e MTRIfuzzy). A principal contribuição dessa tese é a elaboração de uma nova abordagem unificada para orçamento de capital em condições de incerteza que enfatiza os pontos fortes dos métodos modificados do valor presente líquido e da taxa interna de retorno, enquanto contorna os conflitos e as desvantagens individuais dos métodos convencionais. Os resultados mostram que os métodos propostos são mais vantajosos e mais simples de se utilizar que outros métodos de avaliação de investimentos em condições de incerteza.
This thesis presents an alternative approach to capital budgeting, named Fuzzy Modified Methods of Capital Budgeting, for evaluating investment projects under uncertainties. The development of the proposed approach is divided into two phases: in the first stage, a general deterministic model that explicitly provides for the use of the opportunity costs associated with the interim cash flows of a project is established. The implicit assumptions of the modified internal rate of return and modified net present value methods are included in the index of profitability and in the total payback period. The resulting indicators are the modified index of profitability and the modified total payback period. This unified approach has the property to match the decisions of acceptance / rejection of investment projects with same horizons of life and same scales with the decisions of the modified net present value method and therefore maximize shareholder wealth. In the second phase, triangular fuzzy numbers are used to represent the uncertainties of the project variables: cash flows and reinvestment, financing and risk-adjusted discount rates. The resulting indicators are the fuzzy modified net present value, the fuzzy modified internal rate of return, the fuzzy modified index of profitability and the fuzzy modified total payback period. The application of opportunity costs and fuzzy criteria for determining the variables allows obtaining more realists and consistent results with the market conditions. Due to the complexity of the calculations involved, new MS-Excel financial functions are developed by using Visual Basic for Applications: three functions for evaluating projects under conditions of certainty (MVPL, MIL and MTRI) and four functions for evaluating projects under uncertainties (MVPLfuzzy, MTIRfuzzy, MILfuzzy and MTRIfuzzy). The main contribution of this thesis is to develop a unifying approach to capital budgeting under uncertainty that emphasizes the strengths of the methods of modified net present value and modified internal rate of return, while bypassing the individual conflicts and drawbacks of the conventional methods. Results show that the proposed methods are more advantageous and simpler to use than other methods of investment appraisal under uncertainties.
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Maglaras, George K. « Experimental comparison of probabilistic methods and fuzzy sets for designing under uncertainty ». Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/37765.
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Recently, probabilistic methods have been used extensively to model uncertainty in many design optimization problems. An alternative approach for modeling uncertainties is fuzzy sets. Fuzzy sets usually require much less information than probabilistic methods and they rely on expert opinion. In principle, probability theory should work better in problems involving only random uncertainties, if sufficient information is available to model these uncertainties accurately. However, because such information is rarely available, probabilistic models rely on a number of assumptions regarding the magnitude of the uncertainties and their distributions and correlations. Moreover, modeling errors can introduce uncertainty in the predicted reliability of the system. Because of these assumptions and inaccuracies it is not clear if a design obtained from probabilistic optimization will actually be more reliable than a design obtained using fuzzy set optimization. Therefore, it is important to compare probabilistic methods and fuzzy sets and determine the conditions under which each method provides more reliable designs. This research work aims to be a first step in that direction. The first objective is to understand how each approach maximizes reliability. The second objective is to experimentally compare designs obtained using each method.
A cantilevered truss structure is used as a test case. The truss is equipped with passive viscoelastic tuned dampers for vibration control. The structure is optimized by selecting locations for tuning masses added to the truss. The design requirement is that the acceleration at given points on the truss for a specified excitation be less than some upper limit. The properties of the dampers are the primary sources of uncertainty. They are described by their probability density functions in the probabilistic analysis. In the fuzzy set analysis, they are represented as fuzzy numbers.
Two pairs of alternate optimal designs are obtained from the probabilistic and the fuzzy set optimizations, respectively. The optimizations are performed using genetic algorithms. The probabilistic optimization minimizes the system probability of failure. Fuzzy set optimization minimizes the system possibility of failure. Problem parameters (e.g., upper limits on the acceleration) are selected in a way that the probabilities of failure of the alternate designs differ significantly, so that the difference can be measured with a relatively small number of experiments in the lab.
The main difference in the way each method maximizes safety is the following. Probabilistic optimization tries to reduce more the probabilities of failure of the modes that are easier to control. On the other hand, fuzzy set optimization tries to equalize the possibilities of failure of all failure modes.
These optimum probabilistic and fuzzy set designs are then compared in the laboratory. Twenty-nine realizations of each optimum design are tested and the failure rates are measured. The results confirm that, for the selected problems, probabilistic methods can provide designs that are significantly more reliable than designs obtained using fuzzy set methods.Ph. D.
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Gomaa, Ehab. « Environmental balance of mining from seafloor ». Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2014. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-137627.
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The underwater mining has increased in the recent years and the growing awareness of the potential impacts on the environment, as results of increasing the encroachment on the marine environment. Therefore, the debate has increased about how to protect this environment by using the scientific research that\'s relevant to the various environmental effects and developing the equipment used in dredging. There is a wide diversity of underwater mining equipments, such as continuous and non-continuous dredging which used for production of sand, gravel, alluvial deposits and raw material. There are a relation between increasingly dredging activities in the recent years and their impacts on the aquatic environment. These impacts are causes by changes in the topography of the sea floor such as turbidity, noise and other environmental impacts.
Today, there is an international framework of legislation which has been developed for dredging projects. This contains rules and regulations which must be followed by mining companies and have to implemented by national authorities. The European countries also develop their legislation to control on the dredged material which deposit on land and sea. This legislation is constantly changing related to scientific knowledge and increasing the implementation frameworks. Also, the people become more sensitive to emissions that have a negative attitude towards dredging in the neighborhood. In addition, dredging techniques give rise to objections, which eventually led to think in more environmentally-friendly production methods.
After that, the use of dredger is the only alternative in some project. The question is what are the true benefits of these techniques and what is the relation between possible improvements and technological potential as well as the costs. Therefore, it is intended to assess mining techniques in the context of their environmental impact and the costs. There are many systematic approaches which have been used for evaluation and determination of the environmental performances of different dredging equipment, techniques and procedures. Moreover, the new future developments and latest proposals in the dredging industry have been presented. Also, the new proposal to reduce the turbidity and suspended material effect which is the most important environmental impacts during dredging operations has presented.
This research work gives a description of underwater mining techniques and different way of evaluating the dredging equipment’s in term of environmental, economic and social aspects. Also, this work presents two different methods of evaluation. The first approach is the statistical analyses method by using the modern Fuzzy evaluation concept. The second slant is a mathematical accounting method by using the information from the Egyptian case study, in order to reach the most environmental-friendly dredging techniques by taking into account the economic and social point of views.
The final evaluation showed that the suction and bucket ladder dredger are the best choices. The assessment was made after comparing many parameters such as performance, characteristics, working depth, soil types and project area. Also, this work presents two different new practices for extraction of the underwater placer deposits which occur on the Egyptian Mediterranean coast.
The first technique uses floating processing unit in the in situ area, which gets the extracted material and treated it to reduce the transportation costs. The second technique has divided the working area to many stages which make the suction dredger used also for transporting the material into the beach. The pipeline will be the best method of transportation by using the power of suction, which reduce also the transportation costs and also avoid the environmental effects.
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Senge, Robin [Verfasser], et Eyke [Akademischer Betreuer] Hüllermeier. « Machine Learning Methods for Fuzzy Pattern Tree Induction / Robin Senge. Betreuer : Eyke Hüllermeier ». Marburg : Philipps-Universität Marburg, 2014. http://d-nb.info/1059855569/34.
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Horng, Yih-Jen, et 洪一禎. « Fuzzy Information Retrieval Methods Based on Fuzzy Concept Networks ». Thesis, 1996. http://ndltd.ncl.edu.tw/handle/02967377457402835997.
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碩士國立交通大學
資訊科學學系
84
Since the fuzzy concept networks allow us to obtain related information by means of the relevant values between concepts, many fuzzy information retrieval methods based on fuzzy concept networks had been proposed. However,the relevant values between concepts in the traditional fuzzy concept networksare restricted to real values between zero and one. The fuzzy information retrieval methods based on these traditional fuzzy concept networks are not flexible enough in practical applications. In this thesis, we extend the definition of fuzzy concept networks to allow the relevant values between concepts could be interval values or trapezoidal fuzzy numbers. Moreover, four kinds of fuzzy relations between concepts are also provided, i.e. fuzzy positive association, fuzzy negative association, fuzzy generalization, and fuzzy specialization. In this thesis, we also propose fuzzy information retreival methods based on the proposed extended fuzzy concept networks. The proposed methods will make the fuzzy information retrieval systems more flexible to the users.
27
Jeng, Ya-Ju, et 鄭亞竹. « Attribute-Weighted fuzzy clustering methods ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/54336872793242199475.
Texte intégralRésumé :
碩士中原大學
應用數學研究所
95
Since Zadeh proposed fuzzy set theory, fuzzy clustering has been widely studied and applied in a variety of substantive areas. The clustering applications in various areas such as taxonomy, feature analysis, image processing, medicine, neural networks, geology, engineering systems and business, etc. Fuzzy c-means (FCM) are extensions of hard c-means (HCM). FCM has been become the most well-known and powerful method in cluster analysis. In the fuzzy clustering literature, the fuzzy c-means algorithm first proposed by Dunn and then generalized by Bezdek is one of the most efficient ones among fuzzy clustering algorithm. However, fuzzy c-means algorithm take the same assumption that some applications. Often in high dimensional data, many dimensional are irrelevant and can mask existing clusters in noisy data. Sometimes part attributes contribute more than others in deciding the cluster structure. How to distinguish the importance of these attribute? Variable selection and weighting are important approaches in cluster analysis. In this paper we propose a new metric. This proposed metric is more robustic than FCM、AWFCM and AFCM. Thus, we claim that the proposed new metric is more robust than others, which is better one.
28
Yung-Chou et 陳勇洲. « New Methods for Generating Fuzzy Rules for Fuzzy Classification Systems ». Thesis, 2001. http://ndltd.ncl.edu.tw/handle/59759052097981521421.
Texte intégralRésumé :
碩士國立臺灣科技大學
電子工程系
89
The fuzzy classification system is an important application of the fuzzy set theory. The most important task to design fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. There are two main approaches to obtain the fuzzy rules of fuzzy classification systems. One of them is given by experts; the other is through an automatic learning process. In recent years, there are many method have been proposed to generate fuzzy rules from training instances for fuzzy classification systems. In this thesis, we proposed two new algorithms for generating fuzzy rules from training instances. We propose a new algorithm to generate weighted fuzzy rules from training data to deal with classification problems. Firstly, we convert the training data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. Then, we proposed another new algorithm to generate fuzzy rules using the genetic algorithm. Firstly, we divide the training data into several clusters and generate a fuzzy rule for each cluster. Then, we tune the membership functions of fuzzy rules by genetic algorithms. The proposed algorithms can get a higher average classification accuracy rate and generate less fuzzy rules than existing methods.
29
Shu-Kuang, Chang, et 張曙光. « Methods on Testing Hypotheses of Fuzzy Mean and Fuzzy Variance ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/35917831884062870622.
Texte intégralRésumé :
博士國立政治大學
應用數學研究所
95
In many expositions of fuzzy methods, fuzzy techniques are described as an alternative to a more traditional statistical approach. In this paper, we present a class of fuzzy statistical decision process in which testing hypothesis can be naturally reformulated in terms of interval-valued statistics. We provide the definitions of fuzzy mean, fuzzy distance as well as investigation of their related properties. We also give some empirical examples to illustrate the techniques and to analyze fuzzy data. Empirical studies show that fuzzy hypothesis testing with soft computing for interval data are more realistic and reasonable in the social science research. Finally certain comments are suggested for the further studies. We hope that this reformation will make the corresponding fuzzy techniques more acceptable to researchers whose only experience is in using traditional statistical methods. Key words: Membership function, fuzzy sampling survey, fuzzy mean, human thought, t-test, F-test, normally distributed.
30
蔡豐州. « Methods for solving fuzzy goal programming ». Thesis, 1997. http://ndltd.ncl.edu.tw/handle/01158736348985012340.
Texte intégral
31
Chen, Juin-Han, et 陳君涵. « Modularized Fuzzy Ranking Methods and Application ». Thesis, 1999. http://ndltd.ncl.edu.tw/handle/64248335678491531079.
Texte intégralRésumé :
碩士國立清華大學
工業工程與工程管理學系
87
The comparison or ranking of fuzzy numbers has been a very important topic. Many researchers have proposed different methods for ranking fuzzy numbers. However, there are some limitations or insufficiency on the application of these ranking methods. The objective of this study is to develop a generic framework of fuzzy ranking methods so that the ranking methods can be decomposed into modules and be examined systematically. Thus, the users can choose different modules to generate specific fuzzy ranking method depending on different demands. To examine the feasibility and reliability, we used different ranking methods to illustrate the proposed framework. We also discussed the objectives for developing fuzzy ranking methods and proposed the strategies for improving existing methods and developing new methods.
32
Wu, Hong-Cheng, et 吳鴻鎮. « Comparative Study on Fuzzy Modeling Methods ». Thesis, 1996. http://ndltd.ncl.edu.tw/handle/48255832184823208313.
Texte intégralRésumé :
碩士國立海洋大學
電機工程學系
84
This thesis is focused on the investigation of fuzzy modeling. The fuzzy model utilizes a set of fuzzy IF-THEN rules to describethe behavior of systems and is suitable for characterizing the nonlinear behavior of physical systems. In this work we are tostudy three different types of fuzzy models, including theirconstruction and analysis. First we apply the three types ofmodels for representing the input-output data set of some nonlinearsystems. Then the modeling results are analyzed and compared. Finallywe discuss and evaluate the advantages and disadvantages of the threetypes of fuzzy models.
33
Chang, Yu-chaun, et 張昱銓. « New Fuzzy Interpolative Reasoning Methods for Sparse Fuzzy Rule-Based Systems ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/2m6g6n.
Texte intégralRésumé :
博士國立臺灣科技大學
資訊工程系
100
Fuzzy interpolative reasoning is an important research topic for sparse fuzzy rule-based systems. It not only can overcome the drawback of sparse fuzzy rule-based systems, but also can help to reduce the complexity of large fuzzy rule bases for fuzzy rule-based systems. In this dissertation, we present five new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on type-1 fuzzy sets and interval type-2 fuzzy sets, respectively. In the first method of our dissertation, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based system based on the areas of fuzzy sets. The proposed method uses the weighted average method to infer the fuzzy interpolative reasoning results. In terms of the six evaluation indices, the experimental results show the proposed method performs more reasonably than the existing methods. In the second method of our dissertation, we present a new method for multi-variables fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. We apply the proposed method to the temperature prediction problem and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) data. The experimental results show that the proposed method produces better forecasting results than existing methods. In the third method of our dissertation, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. It is based on genetic algorithm (GA)-based weight-learning techniques. The proposed method can deal with fuzzy rule interpolation with weighted antecedent variables. We also present a GA-based weight-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method and the proposed GA-based weight-learning algorithm to deal with the truck backer-upper control problem, multivariate regression problems and time series prediction problems. Based on statistical analysis techniques, the experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed GA-based weight-learning algorithm has statistically significantly smaller error rates than the existing methods. In the fourth method of our dissertation, we present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratios of fuzziness of interval type-2 fuzzy sets. The proposed method can deal with fuzzy rule interpolation based on polygonal interval type-2 fuzzy sets and bell-shaped interval type-2 fuzzy sets. The experimental results show that the proposed method gets more reasonable results than the existing methods. In the fifth method of our dissertation, we present a new method for fuzzy rule interpolation with interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems. We also present a learning algorithm to learn the optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rule interpolation method and the proposed learning algorithm to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rule interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets produces higher accuracy than the existing methods.
34
Ko, Yuan-Kai, et 柯元凱. « New Fuzzy Interpolative Reasoning Methods for Sparse Fuzzy Rule-Based System ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/46036899751346866780.
Texte intégralRésumé :
碩士國立臺灣科技大學
資訊工程系
96
In sparse fuzzy rule-based systems, the fuzzy rule bases usually are incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpola¬tive reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. Moreover, we also extend the α-cuts and transformation techniques to present a new method to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. For multiple antecedent variables fuzzy rules interpolation, the proposed method allows each linguistic variable appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The proposed methods provide a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
35
Yu, Cheng-Hao, et 游承澔. « New Methods for Handling Fuzzy Classification Problems for Fuzzy Classification Systems ». Thesis, 2002. http://ndltd.ncl.edu.tw/handle/38491461139349654318.
Texte intégralRésumé :
碩士國立臺灣科技大學
電子工程系
90
The fuzzy classification system is an important application of the fuzzy set theory. Fuzzy classification systems can deal with perceptual uncertainties in classification problems. In this thesis, we proposed two methods to deal with fuzzy classification problems for fuzzy classification systems. The first method can deal with fuzzy classification problems based on the concept of fuzzy compatibility relations for finding the cluster centers of training instances. The proposed method can get a higher average classification accuracy rate than the existing methods. The second method is based on the exclusion of useless input attributes to generate fuzzy rules from training instances to deal with the Iris data classification problem. It can discard some useless input attributes to improve the average classification accuracy rate. The proposed method can get a higher average classification accuracy rate and can generate fewer fuzzy rules and fewer inputs fuzzy sets in the generated fuzzy rules than the existing methods.
36
Kao, Han-Ying, et 高韓英. « Reasoning with Fuzzy Information : Methods and Applications ». Thesis, 2004. http://ndltd.ncl.edu.tw/handle/47338048153161144371.
Texte intégralRésumé :
博士國立交通大學
資訊管理研究所
92
Reasoning is a major task to an expert system or a decision support system. Three types of reasoning tasks prevail in real-world applications: prediction, diagnosis and planning. Among the various knowledge bases and computation schema, Bayesian networks and influence diagrams are well-known graphical models for reasoning and decision-making under uncertainty. Many algorithms have been designed to answer the queries on a Bayesian network or an influence diagram. However, several limitations persist in the conventional methods. First, all relevant parameters are assumed to be crisp. Second, extra constraints or knowledge regarding belief propagation in Bayesian networks are difficult to embed. Third, diagnosis and planning cannot be completed in the same place. Motivated by the limitations mentioned above, this dissertation extend the traditional Bayesian networks to general Bayesian networks (GBN) that are composed of several components: the set of discrete random nodes, continuous random nodes, decision nodes, crisp parameters, and fuzzy parameters. In addition to the conventional reasoning problems that consider only crisp nodes and crisp parameters, three categories of reasoning are solved as the special cases (subsets) of general Bayesian networks: (1) diagnosis with discrete random nodes and fuzzy parameters; (2) diagnosis and decision-making with discrete random nodes and fuzzy parameters; and (3) diagnosis and decision-making with continuous random nodes in dynamic environments. The distinguished features of this dissertation include: (1) extend the traditional Bayesian networks to general Bayesian networks, including discrete random nodes, continuous random nodes, decision nodes, crisp parameters, and fuzzy parameters. The general Bayesian networks are induced as the general research framework; (2) solve fuzzy reasoning tasks in three subsets of GBN where fuzzy parameters and possibility distributions are considered; (3) consider extra knowledge or constraints for the belief propagation, which are not implemented in the formal knowledge bases; (4) answer the queries from Bayesian networks in dynamic as well as static environments; (5) the reasoning models and methods are applied to the cases from medical informatics and supply chain management. All the applications are developed and illustrated in details.
37
Lu, Hai-Wen, et 陸海文. « A Study of Fuzzy Numbers Ranking Methods ». Thesis, 2001. http://ndltd.ncl.edu.tw/handle/09294615240837080277.
Texte intégralRésumé :
博士國立成功大學
工業管理學系
89
Decision makers usually perform imprecise evaluations for a set of alternatives in an uncertain environment, because of the lack of precise information, such as unquantifiable information, incomplete information, nonobtainable information and partial ignorance. To resolve this problem, fuzzy set theory has been extensively used. Fuzzy numbers are applied to represent the imprecise measurements of different alternatives. This leads that the evaluations of a set of alternatives are actually the ranking of the aggregated fuzzy numbers. The ranking process leads to determine a decision-maker’s preference order of fuzzy numbers. Adopting the concept of alpha-cut, this research specifically develops three ranking methods, namely the total dominance index, the weighted belief measurement index and Integrated Signal/Noise (S/N) index. The total dominance index is defined as the function of the number of alpha-cuts, the index of optimism and the left and right spreads at some alpha — cuts of fuzzy numbers, while the weighted belief measurement index includes the elements in the total dominance index and the weighted average on the basis of alpha—cuts. In addition, the Integrated Signal/Noise index incorporates the signal/noise (S/N) ratio into the weighted belief measurement index. The proposed three ranking methods are simple and efficient in terms of the calculations and comparisons. Unlike the existing integral approaches and area measurements, in the proposed approaches membership functions are not necessary to be known in advance. Only several alpha-cuts are needed for obtaining the index value. In addition, the proposed methods also can be used for ranking nonlinear fuzzy numbers, discrete fuzzy numbers and a pure number.
38
YANG, MING-XIAN, et 楊銘賢. « Lot-sizing methods for fuzzy demand problems ». Thesis, 1993. http://ndltd.ncl.edu.tw/handle/02567642653084711379.
Texte intégral
39
Horng, Yih-Jen, et 洪一禎. « New Knowledge-Based Fuzzy Information Retrieval Methods ». Thesis, 2003. http://ndltd.ncl.edu.tw/handle/90274121500459090863.
Texte intégralRésumé :
博士國立交通大學
資訊科學系
91
By means of the embedded knowledge in knowledge bases, knowledge bases can help information retrieval systems to retrieve relevant documents with respect to the user’s query in a more flexible and more intelligent manner. Since fuzzy concept networks consisting of nodes and directed links are easy to represent the relationships between meaningful entities in the information retrieval environment, many fuzzy information retrieval methods have been proposed to utilize fuzzy concept networks as knowledge bases. However, the relevant values between concept nodes in the traditional fuzzy concept networks are restricted to real values between zero and one. Moreover, the concepts in a traditional fuzzy concept network can be related to each other by only one kind of fuzzy relationship. The fuzzy information retrieval methods based on these traditional fuzzy concept networks are not flexible enough in practical applications. In this dissertation, we firstly propose the concept of fuzzy-valued concept networks to allow the relevant values between concepts to be represented by triangular or trapezoidal fuzzy numbers. We also propose a method to find inheritance hierarchies in the fuzzy-valued concept networks. Then, we further extend the definition of fuzzy-valued concept networks to allow the relevant values between concepts to be represented by fuzzy numbers of arbitrary shapes and propose an information retrieval method based on this kind of fuzzy-valued concept networks. Moreover, we also proposed the definition of multi-relationship fuzzy concept networks, where a concept can be related to another concept by multiple kinds of fuzzy relationships simultaneously. The multi-relationship fuzzy concept networks are very useful in fuzzy information retrieval systems for document retrieval. Furthermore, in order to reduce the effort of constructing multi-relationship fuzzy concept networks, we also propose a method to automatically construct the multi-relationship fuzzy concept networks based on training documents. In this dissertation, we also present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. Firstly, we propose a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster center of each document cluster. Then, we propose a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user’s query for query expansion and to guide the information retrieval systems to retrieve more documents which are relevant to the user’s request. Finally, we present a new method for fuzzy information retrieval based on document terms reweighting techniques. The proposed method modifies the weights of document terms in document descriptor vectors based on the user’s relevance feedback. After modifying the weights of terms in document descriptor vectors, the degrees of satisfaction of relevant documents with respect to the user’s query will increase, and the degrees of satisfaction of irrelevant documents with respect to the user’s query will decrease. Then, the modified document descriptor vectors can be used as personal profiles for future query processing.
40
Lin, Chung-Jie, et 林中傑. « GPS Positioning Accuracy Improvement by Fuzzy Methods ». Thesis, 1997. http://ndltd.ncl.edu.tw/handle/04864117154083500771.
Texte intégralRésumé :
碩士國立臺灣大學
電機工程學系
85
GPS接收機的定位精確度對於使用者而言是很重要的，特別是使用C/A 碼的單一接收機。在本論文中，將提出應用模糊理論於全球定位系統以改 善定位精確度的方法。 針對衛星幾何分佈所造成的精確釋 度(Dilution of Precision, DOP)及接收機通道之訊號雜訊比(Signal- Noise Ratio, SNR)對於定位誤差所造成的影響，於文中將有詳細的探討 。然而，PDOP和SNR之間的關係會彼此矛盾，且具有一定的模糊性，這也 就是為什麼引用模糊方法的原因。實驗的方法大致為，利用定位資料的 PDOP值、SNR值作為模糊處理單元( Fuzzy Processing Unit )的輸入，用 以推論計算該筆定位資料的信賴度因子 (Reliable Factor)。再者， 將此信賴度因子和一預先設定的臨界值比較，若信賴度因子大於該值，則 選用此定位資料。經由此方法，而可以從原始資料中，篩選出較為精確的 定位資料，達到精度改善的目的。 本實驗的方法將分別用於C/A碼單機及DGPS接收機。從實驗的結果可知， 以此方法改善GPS定位的精度，是確實可行的。而其改進之程度對C/A碼單 機是較具效果的。 The positioning accuracy of the GPS receiver is important to the users, especially for those who use the C/A code stand alone receiver. In this thesis,an application of fuzzy set theory to the problem of GPS positioning accuracyimprovement is proposed. The effects of DOP ( Dilution Of Precision ) , SNR (Signal- Noise Ratio) to positioning error are discussed in the content. However,there are tradeoffsamong the PDOP and SNR. That is the reason why we introduced these quantities into fuzzy processing unit. The PDOP and SNR values are used for the fuzzy processing unit to evaluate the reliable factor, which represents the reliability of this position fix. Next, the reliable factor is compared with adesired threshold value. If it exceeds this value, this position fix is adopted. By this way, we can select the more accurate position fixes from the original ones to improve the positioning accuracy. We employed this fuzzy processing on both the C/A code stand alone receiver and the DGPS receiver. Our experimental results will illustrate that fuzzy processing on GPS data can actually reduce the positioning error to a certain extent. The improvements on C/A code single receiver are more obvious.
41
Adam, Stenly Ibrahim, et Stenly Ibrahim Adam. « New Methods for Weighted Fuzzy Interpolated Reasoning and Adaptive Fuzzy Interpolation for Sparse Fuzzy Rule-Based Systems ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/76982182357165874513.
Texte intégralRésumé :
碩士國立臺灣科技大學
資訊工程系
105
Fuzzy interpolative reasoning is a very important research topic in sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems. In the first method, we propose a new transformation-based weighted fuzzy interpolative reasoning method based on the ranking values of polygonal fuzzy sets and the proposed scale and move transformation techniques. The proposed weighted fuzzy interpolative reasoning method is based on the multiple fuzzy rules and multiple antecedent variables fuzzy interpolative reasoning scheme, which can automatically calculate the weight of each fuzzy rule and can automatically calculate the weight of each antecedent variable of the fuzzy rules. The proposed scale and move transformation techniques can deal with singleton fuzzy sets and polygonal fuzzy sets. In the second method, we propose a new adaptive fuzzy interpolative reasoning method based on general representative values of polygonal fuzzy sets and the proposed shift and modification techniques. The proposed adaptive fuzzy interpolative reasoning method includes a new contradiction solving method to get a higher similarity degree between polygonal fuzzy sets of the adaptive fuzzy interpolative reasoning results. The experimental results show that the proposed weighted fuzzy interpolative reasoning method and the proposed adaptive fuzzy interpolation for sparse fuzzy rule-based systems outperforms the existing methods
42
Wei, Shih-Hua, et 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.
Texte intégralRésumé :
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.
43
Hong, Won-Sin, et 洪婉馨. « New Methods for Handling Fuzzy Information Retrieval Problems ». Thesis, 2005. http://ndltd.ncl.edu.tw/handle/02606242659733318827.
Texte intégralRésumé :
碩士國立臺灣科技大學
資訊工程系
93
With the rapid development of information technology, more and more information appears in the network in the form of text documents. In order to help users to get their needed documents, the role of information retrieval systems is more and more important. With the help of an information retrieval system, users can get relevant documents ranking by their relevant degrees. In recent years, some researchers have used averaging operators to deal with the “AND” and “OR” operations of users’ fuzzy queries for fuzzy information retrieval. In this thesis, we present new averaging operators, called weighted power-mean averaging (WPMA) operators, to deal with fuzzy information retrieval. We also use some examples to show the proposed WPMA operators can overcome the drawback of the existing averaging operators and prove the properties of the proposed WPMA operators. Then, we present a prioritized information fusion algorithm for handling fuzzy information retrieval problems. We also present a new method for ranking generalized fuzzy numbers. The proposed methods can improve the performance of information retrieval systems for document retrieval.
44
Chun-YuChen et 陳君諭. « Evaluating Engineering Dispute resolution Methods using Fuzzy AHP ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/94969720622037294549.
Texte intégralRésumé :
碩士國立成功大學
土木工程學系碩博士班
98
The prosperous of construction projects in the domestic market and the complicated construction work division are known to be the major causes lead to complexities and uncertainties in the construction processes from procurement to construction stages. The uncertainties, the complexity further lead to the awareness of fair deal among the stakeholders in which it’s eventually escalates into construction dispute. Construction dispute is the common scenario seen in construction projects in which the duration dispute and also the change order dispute are the most common issues. Dispute resolution methods adopted in Taiwan included conciliation, mediation, arbitration and litigation. Owners and contractors whom are the disputants possess different preferences, expectations and development goals made the collective decision-making more complicated and unable to reach to an agreement. These would eventually double the controversy in the construction dispute and it is difficult to resolves in a quick and effective manner. Previous studies on construction dispute focused on the causes of disputes and disputes resolution procedure. Fewer researches focus on which resolution method prefer by disputants and the most preferable decision-making element in construction dispute. Thus, in this study, the objectives of are to identify the disputants’ most preferable dispute resolution method and also to identify the similarities and the differences in the preferred methods and to deal effectively with the dispute. Analytic Hierarchy Process (AHP) is capable to analyze multi-criteria decision-making (MCDM) problem effectively while fuzzy theory able to solves human subjective or fuzzy thinking characteristics. Fuzzy AHP (FAHP) adopted as the analysis method with the case study focused on the changes of duration and the change order dispute in a highway construction in Taiwan. The main criteria adopted in this study included time, cost, regulation, and effectiveness. The main criteria are then broke down into 11 sub-criteria. In addition, the controversial causes of such (1) when dispute caused by owner, (2) when dispute caused by contractors, and (3) when nobody are held liable for the cause of dispute, as the additional considerations. Finally, analysis on the “first mediate later arbitrate” is discussed. The key finding shows that both disputants prefer conciliation followed by meditation. However, owner dislikes arbitration and contractor dislike litigation. Results obtained from this study are able to guide future decision-making in dispute resolution.
45
Chen, Chao-Dian, et 陳晁典. « New Methods for Handling Forecasting Problems Using Fuzzy Time Series and Fuzzy Logical Relationships ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/38418111128707767479.
Texte intégralRésumé :
碩士國立臺灣科技大學
資訊工程系
97
In this thesis, we present two new methods to handle forecasting problems based on fuzzy time series and fuzzy logical relationships. In the first method, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series. The proposed method constructs the first-order fuzzy logical relationship based on the historical data and fuzzifies the variation of the main factor (TAIEX) and the secondary factor (the Dow Jones, the NASDAQ, the M1b (Taiwan) or their combinations) for forecasting the TAIEX to increase the forecasting accuracy rate. In the second method, we present a new method for the TAIEX prediction, the enrollments of the University of Alabama prediction and the inventory demand prediction based on high-order fuzzy logical relationships. The proposed method constructs high-order fuzzy logical relationships based on the historical data and uses the fixed length of intervals in the universe of discourse for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) prediction, the enrollments of the University of Alabama prediction and the inventory demand prediction. The proposed methods can get higher forecasting accuracy rates than the existing methods.
46
WU, HONG-YI, et 吳虹毅. « Reliability Allocation Based on Fuzzy-delphi and Fuzzy-AHP Methods in Vehicle System Development ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wv2f2h.
Texte intégralRésumé :
碩士南開科技大學
車輛與機電產業研究所
107
The reliability allocation method is used for the sub-level systems of product which should be allocated down the reliability objective during the initial development phase. After selecting and comparing the necessary estimated parameters of the sub-level systems, the weighing values will be determined from the experts. To reduce the cost of reliability works for the new product development program, selecting applicable reliability allocation method has become important work. This study adopts the fuzzy Delphi method (FDM) and Fuzzy Analytical Hierarchy Process (FAHP) to establish two reliability allocated models for the vehicle system. The first proposed FDM model is more objective than the Feasibility-of-objectives method. Instead of giving the points directly with the Linguistic variables. The second proposed Fuzzy AHP model is more simple and easy than the Thurstone-Mosteller allocation method. Instead of mass computations with the Fuzzy computation. Besides, the proposed two models adopt the consistency analytic procedure to confirm the accuracy of weighting values estimated by the experts. We build up the analytic procedure of proposed FDM and FAHP two models successfully. The Experimental results suggests the estimated parameters of weighting values for the reliability allocation in vehicle system and they can be useful in other field product applications.
47
Chen, Ze-jin, et 陳澤金. « New Fuzzy Interpolative Reasoning Methods based on Piecewise Fuzzy Entropies of Fuzzy Sets, Piecewise Fuzzy Entropies of Rough-Fuzzy Sets and the Ratios of Fuzziness of Rough-Fuzzy Sets ». Thesis, 2014. http://ndltd.ncl.edu.tw/handle/70287632518485817267.
Texte intégralRésumé :
碩士國立臺灣科技大學
資訊工程系
102
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. It can overcome the drawbacks of sparse fuzzy rule-based systems and can reduce the complexity of fuzzy rule bases for fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on type-1 fuzzy sets and rough-fuzzy sets, respectively. In the first method of our thesis, we propose a new method for weighted fuzzy interpolative reasoning based on piecewise fuzzy entropies of fuzzy sets. The experimental results show that the proposed weighted fuzzy interpolative reasoning method outperforms the existing methods for dealing with the multivariate regression problems, the Mackey-Glass chaotic time series prediction problem, and the time series prediction problems. In the second method of our thesis, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on piecewise fuzzy entropies and the ratios of fuzziness of polygonal rough-fuzzy sets, where the values of the antecedent variables and the consequence variables in the fuzzy rules are represented by polygonal rough-fuzzy sets. We also propose a method for constructing polygonal rough-fuzzy sets from a set of polygonal fuzzy sets. The experimental results show that the proposed fuzzy interpolative reasoning method based on rough-fuzzy sets gets more reasonable fuzzy interpolative reasoning results than the existing method.
48
BARSACCHI, MARCO. « Fuzzy Methods for Machine Learning. A Big Data Perspective ». Doctoral thesis, 2019. http://hdl.handle.net/2158/1150519.
Texte intégralRésumé :
More than fifty years after its introduction, fuzzy sets theory is still thriving and continues to play a relevant role in a wide number of scientific applications. Nevertheless, while the enrichments that fuzzy logic and set theory can provide are
manifold, the recognition of fuzzy set and logic inside the machine learning community remains rather moderate. In this thesis, we present several approaches aimed at improving machine learning techniques using tools borrowed from fuzzy set theory and logic. Particularly, we try to focus more on the machine learning perspective, thus inviting machine learning researcher to appreciate the modelling strengths of fuzzy set theory.
We begin presenting FDT-Boost, a boosting approach shaped according to the SAMME- Adaboost scheme, which leverages fuzzy binary decision trees as base classifiers; then, we explore a distributed fuzzy random forest DFRF, that leverages the Apache Spark framework, to generate an efficient and effective classifier for big data. We also propose a novel approach for generating, out of big data, a set of fuzzy rule-based classifiers characterised by different optimal trade-offs between accuracy and interpretability. The approach, dubbed DPAES-FDT-GL, extends a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented in the Apache Spark environment. Lastly, we focus on an application, showing how fuzzy systems could be employed in helping medical decision; we propose a novel pipeline to support tumour type classification and rule extraction based on somatic CNV data. The pipeline outputs an interpretable Fuzzy Rule-Based Classifier (FRBC).
Much work remains to be done, and fuzzy set theory has still a big role to play in machine learning.
49
Yu, Chian-Son, et 余強生. « Two Methods for Solving Fuzzy Multi-objective Programming Problems ». Thesis, 1998. http://ndltd.ncl.edu.tw/handle/95311577028241630634.
Texte intégral
50
Chang, Chi-Hao, et 張志豪. « New Methods for Generating Fuzzy Rules from Numerical Data ». Thesis, 2001. http://ndltd.ncl.edu.tw/handle/62727720069770369894.
Texte intégralRésumé :
碩士國立臺灣科技大學
電子工程系
89
The fuzzy classification system is an important application of the fuzzy set theory. Fuzzy classification systems can deal with perceptual uncertainties in classification problems. In order to design a fuzzy classification system, it is an important task to construct the membership function for each attribute and generate fuzzy rules from training instances for handling a specific classification problem. There are two approaches to construct the membership function for each attribute and generate fuzzy rules from training instances. One approach is based on human experts’ assistance, and the other approach is by applying machine learning techniques, such that the fuzzy classification system can construct membership functions and generate fuzzy rules from the training instances automatically. In recent years, many researchers have proposed different methods to construct membership functions and to generate fuzzy rules for handling fuzzy classification problems. However, there are some drawbacks in the existing methods: (1) Some existing methods need human experts to predefine initial membership functions, i.e., these methods can not construct membership functions from the training data set fully automatically. (2) Some existing methods are too complicated and need a lot of computation time. (3) Some existing methods generate too many fuzzy rules. In this thesis, we proposed two methods to construct the membership function for each attribute and to generate fuzzy rules automatically from training instances for handling fuzzy classification problems. The first method is based on the exclusion of attribute terms that can achieve a higher average classification accuracy rate and generate less fuzzy rules than the existing methods. The second method generates weighted fuzzy rules from training instances that can construct membership functions automatically without any human experts’ interaction and can generate less fuzzy rules than the existing methods.