Dissertations / Theses on the topic 'Fuzzy sets'
To see the other types of publications on this topic, follow the link: Fuzzy sets.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Fuzzy sets.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Naman, Saleem Muhammad. "Eigen Fuzzy Sets of Fuzzy Relation with Applications." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4060.
Full textAbstract:
Eigen fuzzy sets of fuzzy relation can be used for the estimation of highest and lowest levels of involved variables when applying max-min composition on fuzzy relations. By the greatest eigen fuzzy sets (set which can be greater anymore) maximum membership degrees of any fuzzy set can be found, with the help of least eigen fuzzy set (set which can be less anymore) minimum membership degrees of any fuzzy sets can be found as well.The lowest and highest level, impact or e ffect of anything can be found by applying eigen fuzzy set theory. The implicational aspect of this research study is medical and customer satisfaction level measurement. By applying methods of eigen fuzzy set theory the e ffectiveness of medical cure and customer satisfaction can be found with high precision.
2
Rabetge, Christian. "Fuzzy Sets in der Netzplantechnik /." Wiesbaden : Dt. Univ.-Verl, 1991. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=002624347&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Full text
3
VISA, SOFIA. "FUZZY CLASSIFIERS FOR IMBALANCED DATA SETS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1182226868.
Full text
4
Meyer, David, and Kurt Hornik. "Generalized and Customizable Sets in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/1062/1/document.pdf.
Full textAbstract:
We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (author´s abstract)Series: Research Report Series / Department of Statistics and Mathematics
5
Hornik, Kurt, and David Meyer. "Generalized and Customizable Sets in R." American Statistical Association, 2009. http://epub.wu.ac.at/4002/1/sets.pdf.
Full textAbstract:
We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy
(multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (authors' abstract)
6
Li, Ying. "Probabilistic interpretations of fuzzy sets and systems." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11619.
Full text
7
John, Robert. "Perception modelling using type-2 fuzzy sets." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/5856.
Full textAbstract:
Type-1 fuzzy logic has, for over thirty years, provided an approach for modelling uncertainty and imprecision. This methodology has been highly successful with a history of successful applications in a number of areas - particularly control. However, type-1 fuzzy systems are essentially `crisp' in nature. This is not only paradoxical but also raises concerns for knowledge representation and inferencing. In particular type-1 fuzzy logic is flawed when representing perceptions such as colour, beauty, comfort etc. since these perceptions do not have a measurable domain. This fundamental paradox is tackled in this research by employing a type-2 fuzzy paradigm. The investigation of the type-2 approach concludes that the uncertainty or imprecision that exists in most real problems can be more effectively modelled by a type-2 approach. The research reported in this thesis explores the properties of type-2 fuzzy sets as well as showing how useful they can be for knowledge representation and inferencing. It is shown that type-2 fuzzy sets have an important role to play in modelling perceptions. Results are given of using type-2 fuzzy sets to represent perceptions of a medical expert for shin image analysis indicating that the type-2 fuzzy paradigm is particularly helpful for perception representation. A methodology has been developed that allows linguistic inputs to an adaptive system that implements a type-2 fuzzy system(the Adaptive Fuzzy Perception Learner (AFPL)). In this thesis, the rationale and full mathematical detail of the AFPL is presented. The approach has been applied successfully to the, so called, linguistic AND (analogous to the Boolean AND) as an aid to illustrating the methodology. Results are presented of applying the method to a real problem of classifying the acceptability of a car based on perceptions that describe certain features of the car. The AFPL is applied to this large, complex, set of data where the inputs to the network are linguistic. A detailed evaluation of the AFPL is given with recommendations for effective use of the AFPL. The results indicate that we now, truly, have an approach for learning the perceptions and relations in a type-2 fuzzy system
8
Mahlasela, Zuko. "Finite fuzzy sets, keychains and their applications." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005220.
Full textAbstract:
The idea of keychains, an (n+1)-tuple of non-increasing real numbers in the unit interval always including 1, naturally arises in study of finite fuzzy set theory. They are a useful concept in modeling ideas of uncertainty especially those that arise in Economics, Social Sciences, Statistics and other subjects. In this thesis we define and study some basic properties of keychains with reference to Partially Ordered Sets, Lattices, Chains and Finite Fuzzy Sets. We then examine the role of keychains and their lattice diagrams in representing uncertainties that arise in such problems as in preferential voting patterns, outcomes of competitions and in Economics - Preference Relations.
9
Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.
Full text
10
Jensen, Richard. "Combining rough and fuzzy sets for feature selection." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/24740.
Full textAbstract:
Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. FS techniques have also been applied to small and medium-sized datasets in order to locate the most informative features for later use. Many feature selection methods have been developed and are reviewed critically in this thesis, with particular emphasis on their current limitations. The leading methods in this field are presented in a consistent algorithmic framework. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in FS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based feature selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This thesis proposes and develops an approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. Complexity analysis of the underlying algorithms is included. FRFS is applied to two domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other FS techniques in the comparative study. Based on the new fuzzy-rough measure of feature significance^ further development of the FRFS technique is presented in this thesis. This is developed from the new area of feature grouping that considers the selection of groups of attributes in the search for the best subset. A novel framework is also given for the application of ant-based search mechanisms within feature selection in general, with particular emphasis on its employment in FRFS. Both of these developments are employed and evaluated within the complex systems monitoring application.
11
Jaffal, Hussein, and Cheng Tao. "Multiple Attributes Group Decision Making by Type-2 Fuzzy Sets and Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2659.
Full textAbstract:
We are living in a world full of uncertainty and ambiguity. We usually ask ourselves questions that we are uncertain about their answers. Is it going to rain tomorrow? What will be the exchange rate of euro next month? Why, where and how should I invest? Type-1 Fuzzy sets are characterized by the membership function whose value for a given element x is said to be the grade of membership having a value in the interval [0, 1]. In addition, type-1 fuzzy sets have limited capabilities to deal with uncertainty. In our thesis, we study another concept of a fuzzy description of uncertainty which is called Type-2 fuzzy sets. According to this concept, for any given element x, we can’t speak of an unambiguously specified value of the membership function. Moreover, Type-2 fuzzy sets constitute a powerful tool for handling uncertainty. The aim of our thesis is to examine the potential of the Type-2 fuzzy sets especially in decision making. So, we present basic definitions concerning Type-2 fuzzy sets, and operations on these sets are to be discussed too. Then, Type-2 fuzzy relations and methods of transformation of Type-2 fuzzy sets will be introduced. Also, the theory of Type-2 Fuzzy sets will serve for the construction of the fuzzy inference system. Finally, we utilize interval type-2 fuzzy sets in the application of Multiple Attributes Group Decision Making which is called TOPSIS.
12
Nejatali, Abdolhossein. "Electrical impedance tomography with neural networks and fuzzy sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq23645.pdf.
Full text
13
Khoshnoud, Farbod. "A novel modal analysis method based on fuzzy sets." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/380.
Full textAbstract:
A novel method of vibration modelling is proposed in this thesis. This method involves estimating the mode shapes of a general structure and describing these shapes in terms of fuzzy membership functions. These estimations or initial guesses are based on engineer's experience or physical insight into natural mode shapes assisted by end and boundary conditions and some rules. The guessed mode shapes were referred to as Mode Shape Forms (MSFs). MSFs are approximate mode shapes, therefore there are uncertainties involve with their values where this uncertainty is expressed by fuzzy sets. The deflection or displacement magnitude of the mode shape forms are described with Zero, Medium, and Large fuzzy linguistic terms and constructed using fuzzy membership functions and rules. Fuzzy rules are introduced for each MSF. In that respect fuzzy membership functions provides a means of dealing with uncertainty in measured data, it gives access to a large repertoire of tools available in fuzzy reasoning field. The second stage of the process addresses the issues of updating these curves by experimental data. This involves performing experimental modal analysis. The mode shapes derived from experimental FRFs collect a limited number of sampling points. When the fuzzy data is updated by experimental data, the method proposes that the points of the fuzzy data correspond to the sampling points of FRF are to be replaced by the experimental data. Doing this creates a new fuzzy curve which is the same as the previous one, except at those points. In another word a 'spiked' version of the original fuzzy curve is obtained. In the last stage of this process, neural network is used to 'learn' the spiked curve. By controlling the learning process (by preventing it from overtraining), an updated fuzzy curve is generated that is the final version of the mode shape. Examples are presented to demonstrate the application of the proposed method in modelling of beams, a plate and a structure (a three beams frame). The method is extended to evaluate the error where a wrong MSF is assumed for the mode shape. In this case the method finds the correct MSF among available guessed MSFs. A further extension of the method is proposed for cases where there is no guess available for the mode shape. In this situation the 'closest' MSF is selected among available MSFs. This MSF is modified by correcting the fuzzy rules that is used in constructing of the fuzzy MSF. Using engineering experience, heuristic knowledge and the developed MSF rules in this method are the capabilities that cannot be provided with any artificial intelligent system. This provides additional advantage relative to vibration modelling approaches that have been developed until now. Therefore this method includes all aspects of an effective analysis such as mixed artificial intelligence and experimental validation, plus human interface/intelligence. Another advantage is, MSF rules provide a novel approach in vibration modelling where enables the method to start and operate with unknown input parameters such as unknown material properties and imprecise structure dimensions. Hence the classical computational procedures of obtaining the vibration behaviour of the system, from these inputs, are not used in this approach. As a result, this method avoids the time consuming computational procedure that exhibit in existing vibration modelling methods. However, the validation procedure, using experimental tests (modal testing) is the same acceptable procedure that is used in any other available methods which proves the accuracy of the method.
14
Danker-McDermot, Holly. "A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/86.
Full textAbstract:
The project objective in this work is to create an accurate cost estimate for NASA engine tests at the John C. Stennis Space Center testing facilities using various combinations of fuzzy and neural systems. The data set available for this cost prediction problem consists of variables such as test duration, thrust, and many other similar quantities, unfortunately it is small and incomplete. The first method implemented to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inference system (ANFIS). The second method is a two stage system that uses various ANFIS with either single or multiple inputs for a cost estimate whose outputs are then put through a backpropagation trained neural network for the final cost prediction. Finally, method 3 uses a radial basis function network (RBFN) to predict the engine test cost.
15
Makamba, B. B. "Studies in fuzzy groups." Thesis, Rhodes University, 1993. http://hdl.handle.net/10962/d1005229.
Full textAbstract:
In this thesis we first extend the notion of fuzzy normality to the notion of normality of a fuzzy subgroup in another fuzzy group. This leads to the study of normal series of fuzzy subgroups, and this study includes solvable and nilpotent fuzzy groups, and the fuzzy version of the Jordan-Hõlder Theorem. Furthermore we use the notion of normality to study products and direct products of fuzzy subgroups. We present a notion of fuzzy isomorphism which enables us to state and prove the three well-known isomorphism theorems and the fact that the internal direct product of two normal fuzzy subgroups is isomorphic to the external direct product of the same fuzzy subgroups. A brief discussion on fuzzy subgroups generated by fuzzy subsets is also presented, and this leads to the fuzzy version of the Basis Theorem. Finally, the notion of direct product enables us to study decomposable and indecomposable fuzzy subgroups, and this study includes the fuzzy version of the Remak-Krull-Schmidt Theorem.
16
Burton, Michael Howard. "Fuzzy uniform spaces." Thesis, Rhodes University, 1992. http://hdl.handle.net/10962/d1005222.
Full textAbstract:
For a fuzzy uniform space, the notion of a Cauchy prefilter, a precompact fuzzy set, a complete fuzzy set and a bounded fuzzy set are defined in such a way that these notions are good extensions of the corresponding notions for a uniform space. A theory of fuzzy uniform spaces is developed which generalises the theory of uniform spaces.
17
Matutu, Phethiwe Precious. "(L, M)-fuzzy topological spaces." Thesis, Rhodes University, 1992. http://hdl.handle.net/10962/d1005224.
Full textAbstract:
The objective of this thesis is to develop certain aspects of the theory of (L,M)-fuzzy topological spaces, where L and M are complete lattices (with additional conditions when necessary). We obtain results which are to a large extent analogous to results given in a series of papers of Šostak (where L = M = [0,1]) but not necessarily with analogous proofs. Often, our generalizations require a variety of techniques from lattice theory e.g. from continuity or complete distributive lattices.
18
Leitch, Donald Dewar. "A new genetic algorithm for the evolution of fuzzy sets." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318473.
Full text
19
Wang, Haibin. "Interval Neutrosophic Sets and Logic: Theory and Applications in Computing." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/2.
Full textAbstract:
A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study.
20
Soderstrom, David. "Fuzzy logic modeling and intelligent sliding mode control techniques for the individualization of theophylline therapy to pediatric patients." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/19097.
Full text
21
GonzaÌlez, RodriÌguez IneÌs. "Automated prototype induction." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251139.
Full text
22
Qiu, Fenglian. "An expert system approach to modelling and planning software product assessment and certification." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259646.
Full text
23
Jacot-Guillarmod, Paul. "Sobriety of crisp and fuzzy topological spaces." Thesis, Rhodes University, 2004. http://hdl.handle.net/10962/d1005228.
Full textAbstract:
The objective of this thesis is a survey of crisp and fuzzy sober topological spaces. We begin by examining sobriety of crisp topological spaces. We then extend this to the L- topological case and obtain analogous results and characterizations to those of the crisp case. We then brie y examine semi-sobriety of (L;M)-topological spaces.
24
Wang, Haibin. "Interval neutrosophic sets and logic theory and applications in computing /." unrestricted, 2005. http://etd.gsu.edu/theses/available/etd-11172005-131340/.
Full textAbstract:
Thesis (Ph. D.)--Georgia State University, 2005.1 electronic text (119 p. : ill.) : digital, PDF file. Title from title screen. Rajshekhar Sunderraman, committee chair; Yan-Qing Zhang, Anu Bourgeois, Lifeng Ding, committee members. Description based on contents viewed Apr. 3, 2007. Includes bibliographical references (p. 112-119).
25
Swartz, Andre Michael. "Methods for designing and optimizing fuzzy controllers." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1005226.
Full textAbstract:
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.
26
Khuman, Arjab Singh. "The quantification of perception based uncertainty using R-fuzzy sets and grey analysis." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/14225.
Full textAbstract:
The nature of uncertainty cannot be generically defined as it is domain and context specific. With that being the case, there have been several proposed models, all of which have their own associated benefits and shortcomings. From these models, it was decided that an R-fuzzy approach would provide for the most ideal foundation from which to enhance and expand upon. An R-fuzzy set can be seen as a relatively new model, one which itself is an extension to fuzzy set theory. It makes use of a lower and upper approximation bounding from rough set theory, which allows for the membership function of an R-fuzzy set to be that of a rough set. An R-fuzzy approach provides the means for one to encapsulate uncertain fuzzy membership values, based on a given abstract concept. If using the voting method, any fuzzy membership value contained within the lower approximation can be treated as an absolute truth. The fuzzy membership values which are contained within the upper approximation, may be the result of a singleton, or the vast majority, but absolutely not all. This thesis has brought about the creation of a significance measure, based on a variation of Bayes' theorem. One which enables the quantification of any contained fuzzy membership value within an R-fuzzy set. Such is the pairing of the significance measure and an R-fuzzy set, an intermediary bridge linking to that of a generalised type-2 fuzzy set can be achieved. Simply by inferencing from the returned degrees of significance, one is able to ascertain the true significance of any uncertain fuzzy membership value, relative to other encapsulated uncertain values. As an extension to this enhancement, the thesis has also brought about the novel introduction of grey analysis. By utilising the absolute degree of grey incidence, it provides one with the means to measure and quantify the metric spaces between sequences, generated based on the returned degrees of significance for any given R-fuzzy set. As it will be shown, this framework is ideally suited to domains where perceptions are being modelled, which may also contain several varying clusters of cohorts based on any number of correlations. These clusters can then be compared and contrasted to allow for a more detailed understanding of the abstractions being modelled.
27
Dimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "Fuzzy voting in clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/742/1/document.pdf.
Full textAbstract:
In this paper we present a fuzzy voting scheme for cluster algorithms. This fuzzy voting method allows us to combine several runs of cluster algorithms resulting in a common fuzzy partition. This helps us to overcome instabilities of the cluster algorithms and results in a better clustering.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
28
Jenssen, Arne. "Unscharfe Zahlen in der Finanzwirtschaft : Fuzzy Sets zur Erfassung von Unsicherheit /." Göttingen : Cuvillier, 1999. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=008771389&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Full text
29
Beers, Suzanne M. "An intelligent hierarchical decision architecture for operational test and evaluation." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15423.
Full text
30
Talwanga, Matiki. "The principle of inclusion-exclusion and möbius function as counting techniques in finite fuzzy subsets." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005227.
Full textAbstract:
The broad goal in this thesis is to enumerate elements and fuzzy subsets of a finite set enjoying some useful properties through the well-known counting technique of the principle of inclusion-exclusion. We consider the set of membership values to be finite and uniformly spaced in the real unit interval. Further we define an equivalence relation with regards to the cardinalities of fuzzy subsets providing the Möbius function and Möbius inversion in that context.
31
Touz'e, Patrick A. "Applications of fuzzy logic to mechanical reliability analysis /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03142009-040345/.
Full text
32
Talwanga, Matiki. "Counting of finite fuzzy subsets with applications to fuzzy recognition and selection strategies." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018186.
Full textAbstract:
The counting of fuzzy subsets of a finite set is of great interest in both practical and theoretical contexts in Mathematics. We have used some counting techniques such as the principle of Inclusion-Exclusion and the Mõbius Inversion to enumerate the fuzzy subsets of a finite set satisfying different conditions. These two techniques are interdependent with the M¨obius inversion generalizing the principle of Inclusion-Exclusion. The enumeration is carried out each time we redefine new conditions on the set. In this study one of our aims is the recognition and identification of fuzzy subsets with same features, characteristics or conditions. To facilitate such a study, we use some ideas such as the Hamming distance, mid-point between two fuzzy subsets and cardinality of fuzzy subsets. Finally we introduce the fuzzy scanner of elements of a finite set. This is used to identify elements and fuzzy subsets of a set. The scanning process of identification and recognition facilitates the choice of entities with specified properties. We develop a procedure of selection under the fuzzy environment. This allows us a framework to resolve conflicting issues in the market place.
33
Wasques, Vinícius Francisco [UNESP]. "Lógica Fuzzy aplicada à geologia." Universidade Estadual Paulista (UNESP), 2015. http://hdl.handle.net/11449/132722.
Full textAbstract:
Made available in DSpace on 2016-01-13T13:27:45Z (GMT). No. of bitstreams: 0
Previous issue date: 2015-07-08. Added 1 bitstream(s) on 2016-01-13T13:31:37Z : No. of bitstreams: 1
000855574.pdf: 806510 bytes, checksum: 5165b6086fbf8e4db7aeff948269d0e3 (MD5)Neste trabalho são apresentadas algumas de nições básicas da Teoria de Conjuntos Fuzzy e alguns exemplos teóricos que, na maioria dos textos, são deixados como exercí- cio para o leitor. Dessa forma, pretende-se que o texto que mais didático e completo, podendo ser aproveitado para cursos introdutórios da teoria. Algumas aplicações, voltadas para a área de Geologia, também são apresentadas. Destacamos a proposta de modelagem realizada utilizando-se informações geofísicas [1] e um sistema baseado em regras fuzzy para o estudo de locais na região de Rio Claro (São Paulo - Brasil) que são mais propícios para se perfurar poços com boas vazões
This work presents some basic de nitions of fuzzy set theory and some theoretical examples that in most of the texts are left as an exercise for the reader. Thus, it is intended that the text is more didactic, complete and can be used to introductory courses theory . Some applications, focused on the Geology eld, are also presented. Highlight the proposed modeling performed using geophysical [1] and a system based on fuzzy rules for study sites in Rio Claro region (São Paulo - Brazil) that are more conducive to drill wells with good flow
34
Wasques, Vinícius Francisco. "Lógica Fuzzy aplicada à geologia /." Rio Claro, 2015. http://hdl.handle.net/11449/132722.
Full textAbstract:
Orientador: Renata Zotin Gomes de OliveiraBanca: Magda da Silva Peixoto
Banca: Laécio Carvalho de Barros
Resumo: Neste trabalho são apresentadas algumas de nições básicas da Teoria de Conjuntos Fuzzy e alguns exemplos teóricos que, na maioria dos textos, são deixados como exercí- cio para o leitor. Dessa forma, pretende-se que o texto que mais didático e completo, podendo ser aproveitado para cursos introdutórios da teoria. Algumas aplicações, voltadas para a área de Geologia, também são apresentadas. Destacamos a proposta de modelagem realizada utilizando-se informações geofísicas [1] e um sistema baseado em regras fuzzy para o estudo de locais na região de Rio Claro (São Paulo - Brasil) que são mais propícios para se perfurar poços com boas vazões
Abstract: This work presents some basic de nitions of fuzzy set theory and some theoretical examples that in most of the texts are left as an exercise for the reader. Thus, it is intended that the text is more didactic, complete and can be used to introductory courses theory . Some applications, focused on the Geology eld, are also presented. Highlight the proposed modeling performed using geophysical [1] and a system based on fuzzy rules for study sites in Rio Claro region (São Paulo - Brazil) that are more conducive to drill wells with good flow
Mestre
35
Hu, Yanting. "Advanced control system for stand-alone diesel engine driven-permanent magnet generator sets." Thesis, De Montfort University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366632.
Full text
36
Chen, Ze-jin, and 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.
Full textAbstract:
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.
37
Lee, Shi-rui, and 李思銳. "Correlation on Fuzzy Sets." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/30919615054273583554.
Full textAbstract:
碩士淡江大學
資訊工程學系
85
It is very common in statistical analysis of data to find the correlation between two variables, the correlation coefficients defined on ordinary crisp sets have been discussed in the conventional statistics. What we are interested is finding the correlation between fuzzy sets, which can tell us the relationship between the fuzzy sets. In this paper, we discussed the correlation for fuzzy data by adopting the concepts from the conventional statistics, rather than defining the correlation on the intuitionistic fuzzy sets like most of the previous works. The value computed from our formula not only provides us the strength of the relationship of two fuzzy sets, but also shows that the fuzzy sets are positively or negatively correlated. For our definition is based on the conventional statistics, the value of correlation coefficient will lay in real interval [-1,1]. If the correlation coefficient is near 1, that means there is a strong positive correlation between the fuzzy sets. If the correlation coefficient is near -1, that means there is a strong negative correlation between the fuzzy sets. If correlation coefficient is 0, we say that there is no correlation between two fuzzy sets. In our paper, we not only define the correlation between fuzzy sets but also prove it is true and obey the properties of the correlation coefficient between ordinary crisp sets. In the last part, we use an example to explain our definition. Besides the definition of correlation coefficient on fuzzy sets, we have proved a theorem that the value computed from our formula is within [-1,1]. Finally we have used a random sample of size 593 to demonstrate our method.
38
Lin, Yu-Cheng, and 林育正. "A fuzzy classifier on fuzzy partially-ordered sets." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/71353875118057599416.
Full textAbstract:
碩士國立中正大學
資訊工程所
93
We approached learning in a unified manner by considering partly ordered sets and, in particular mathematical lattices, as the learning domain. Lattice theory has been employed practically in the past in various contexts including logic, discrete mathematics, and computer science. Lattice theory is also employed for rule learning. The work here maintains an established lattice theory terminology, produces new theoretical results, and gains new insights while demonstrating pilot experimental results. Recently, the semantic web is developing rapidly. The kernel of the semantic web has been an ontology which is also a lattice structure, and we expect that our results would be useful for automatically learning the classes of this ontology. First Degree Entailment (FDE) is a kind of 4-valued logic. We extend (FDE) and design two kinds of L-fuzzy sets, FDE sets and fFDE sets. The notion of a fuzzy lattice extends traditional lattice theory by using fuzzy sets. We propose fFDE lattices by combining fFDE sets and fuzzy lattices. This new kind of fuzzy lattice has higher ability to describe membership. Using this framework, we can solve the problem information loss. We also designed a learning scheme combined with the fFDE lattice framework. This new classifier has features of rapid learning and good performance.
39
Knoth, Antonia Nissen Volker. "Fuzzy Sets und künstliche Agentensysteme /." 2006. http://www.gbv.de/dms/ilmenau/abs/509820190knoth.txt.
Full text
40
Lin, Nancy Pei-ching, and 林丕靜. "Correlation Analysis of Fuzzy Sets." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/04600114630994536322.
Full text
41
Barman, Dipto, and Dipto Barman. "Adaptive Fuzzy Interpolative Reasoning Based on Polygonal Fuzzy Sets and Adaptive Weighted Fuzzy Interpolative Reasoning Based on Interval Type-2 Fuzzy Sets." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kj8gm2.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
107
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and interval type-2 fuzzy sets, respectively. In the first method of our thesis, we propose a new adaptive fuzzy interpolative reasoning method based on contradiction measures between polygonal fuzzy sets and novel move and transformation techniques. The proposed adaptive fuzzy interpolative reasoning method performs fuzzy interpolative reasoning using the multiple fuzzy rules with multiple antecedent variables fuzzy interpolative reasoning scheme and solves the contradictions after the fuzzy interpolative reasoning processes based on contradiction measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolative reasoning method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on representative values and similarity measures of interval type-2 fuzzy sets. The experimental results show that the proposed adaptive weighted fuzzy interpolative reasoning method can overcome the drawbacks of the existing adaptive fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems.
42
Tai, Chang, and 張泰. "Rock Mass Classification Using Fuzzy Sets." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/65391973909133763180.
Full text
43
"Fuzzy semigroups and fuzzy implicative algebra." 2004. http://library.cuhk.edu.hk/record=b6073743.
Full textAbstract:
Lee Shuk Yee."October 2004."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (p. 87-92)
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
44
Chen, Chia-Ling, and 陳佳伶. "New Fuzzy Interpolative Reasoning Methods Based on Ranking Values of Polygonal Fuzzy Sets, Automatically Generated Weights of Fuzzy Rules and Similarity Measures Between Polygonal Fuzzy Sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47496074659379726144.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
103
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and the ranking values of polygonal fuzzy sets. In the first method of our thesis, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. The experimental results show that the proposed method can overcome the drawbacks of the existing fuzzy interpolative reasoning methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive fuzzy interpolation method based on ranking values of polygonal fuzzy sets and similarity measures between polygonal fuzzy sets. The proposed adaptive fuzzy interpolation method performs fuzzy interpolative reasoning using multiple fuzzy rules with multiple antecedent variables and solves the contradictions after the fuzzy interpolative reasoning processes based on similarity measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolation method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
45
Chu, Hsiao-lan, and 朱筱嵐. "Applying Fuzzy Sets and Rough Sets Theories on Multi-Criteria Decision Making." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/81123517228237267059.
Full textAbstract:
碩士義守大學
資訊管理學系碩士班
93
Decision-makers are often met with many decision-making problems which are complex and uncertain in daily life. Due to this, it is very difficult for them to make a proper selection or decision based on individual subjective judgement. With closer cooperation between the decision-makers and schemers, the actual demands of problems can be realized. Therefore, this study proposes a fuzzy decision-making method to evaluate non- quantitative fuzzy decision problems. First, when encountering decision-making problems of fuzzy multi-criteria, decision-makers evaluate candidates in terms of criteria importance. High important criteria are selected as the evaluation index by using the TOPSIS method and linguistic variables which have already been set. Second, this study conducts the performance evaluation for all projects to acquire the whole fuzzy evaluation value of each project. And finally, this study utilizes a rough sets theory to cluster the candidates after defuzzination. According to the results of those clusters, this model gives the decision-maker a point of reference. This study proposes a qualitative multi-criteria evaluation method to evaluate qualitative decision problems with multiple qualitative criteria and multiple decision-makers (experts). This study uses two non-quantitative evaluations as examples: (1) qualitative multi-criteria evaluation for a bank’s examination of the evaluation of loan applicants’ credit; and (2) partial-quantitative multi-criteria evaluation for restaurant service examination. According to the algorithm of a fuzzy decision-making method and rough sets theory, this study introduces an objective evaluation model and offers references for further study by other scholars.
46
Chiang, Tai-Wei, and 江泰緯. "Intelligent Neuro-Fuzzy Computing with Complex Fuzzy Sets and ARIMA Models." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/25481675924809065134.
Full textAbstract:
博士國立中央大學
資訊管理學系
102
Ever since the initiate of the theory of complex fuzzy sets (CFSs), a new vision has dawned upon fuzzy systems and their variants. Although there has been considerable development made in determining the properties of CFSs, the research on complex fuzzy system designs and applications of this concept is found rarely. In this dissertation, we present a novel self-organizing complex neuro-fuzzy intelligent approach using CFSs for the applications of system modeling. The proposed approach integrates a complex neuro-fuzzy system (CNFS) using CFSs and auto-regressive integrated moving average (ARIMA) models to form the proposed computing model, called the CNFS-ARIMA. A class of Gaussian complex fuzzy sets is proposed to describe the premise parts of fuzzy If-Then rules, whose consequent parts are specified by ARIMA models. A CFS is an advanced fuzzy set whose membership degrees are complex-valued within the unit disc of the complex plane, expanding the capability of membership description. With the nature of CFS, the proposed CNFS models have excellent nonlinear mapping capability. Moreover, the output of CNFS-ARIMA is complex-valued, of which the real and imaginary parts can be used for two different functional mappings, respectively. This is the so-called dual-output property. For the formation of CNFS-ARIMA, structure learning and parameter learning are involved to self-organize and self-tune the proposed model. For the structure learning phase, a FCM-based splitting algorithm (FBSA) is used to automatically determine the initial knowledge base of the CNFS-ARIMA. The PSO-RLSE hybrid learning algorithm is proposed for the purpose of fast learning, integrating the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE). A number examples of time series are used to test the proposed approach, whose results are compared with those by other approaches. Moreover, real-world applications of system modeling including function approximation and time series are used for the proposed approach to perform the dual-output forecasting experiments. The experimental results indicate that the proposed approach shows excellent performance.
47
CHIU, CHIH-HUI, and 邱智煇. "The study of fuzziness for fuzzy sets." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/677kqk.
Full textAbstract:
博士國立中央大學
電機工程研究所
88
In this dissertation, fuzzy sets'' fuzziness is our main study topic. By the way, the lattice of fuzzy numbers and the stability of fuzzy systems are also discussed. In Chapter 2, we investigate the entropy relationship between two same type of fuzzy sets and study some properties of the information energy. Then, the relationship between the information energy and the entropy of a fuzzy set is derived. Chapter 3 and Chapter 4 consider the entropy change of fuzzy numbers through arithmetic operations and function mapping. Several simple formulas to get the entropy value for the fuzzy numbers'' sum and for the extension principle are proposed respectively. Chapter 5 proposes an new idea called "entropy unit" to get any fuzzy set''s entropy value easily and quickly. Moreover, Chapter 6 try to simplify the operations of MIN and MAX of fuzzy numbers such that the operations of MIN and MAX can be implemented easily and quickly.
48
Lin, Yung-Fu, and 林永富. "CONCEPT COMMUNICATION BASED ON CONCEPTUAL FUZZY SETS." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/61618726299893227229.
Full textAbstract:
碩士國立交通大學
電機與控制工程系
88
Concept communication is an important issue of man machine interface. It provides the smooth communication between man and system. This thesis introduces conceptual fuzzy set (CFS) to represent the abstract concepts and concrete concepts in the real world. Concept mapping must be bidirectional. Mapping from abstract concepts to concrete concepts is considered as concept recognition. Mapping from concrete concepts to abstract concepts is considered as concept interpretation. We propose several mapping schemes to relate these two type concepts. The fuzzy relation equation approach is first applied for the concept mapping. The forward and backward mappings of concepts are archived by adopting two different fuzzy relation equations, respectively. We apply the genetic algorithm and fuzzy delta rule to learn the relation matrix of fuzzy relation equation. Their performances are not acceptable. In a functional mapping respective instead, the multilayer perceptron neural network is utilized to the concept mapping problem. BP algorithm is adopted to learn the weight matrix in the multilayer perceptron neural network. The backward mapping of concepts is achieved by adopting another MLP neural network. The result of concept mapping by MLP neural network has demonstrated that the MLP network is an effective scheme for concept communication.
49
Mahlasela, Zuko. "Finite fuzzy sets, keychains and their applications /." 2007. http://eprints.ru.ac.za/1605/.
Full text
50
Lin, Der-Chen, and 林德成. "On measures of type-2 fuzzy sets." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/42726357119000209383.
Full textAbstract:
博士中原大學
應用數學研究所
96
Abstract In a practical complex system, humans sometimes use only binary logic theory for deducing some objects or information which is not sufficient to explain all situations. Thus, a fuzzy concept can be utilized for assisting deductions. As for some unclear, uncertain, and incomplete information, they can be compared and screened by measured value of fuzzy set. Additionally, the new definition and theorem of type-2 fuzzy sets proposed by Mendel and John in recent years have been widely studied and spread, and applied to many fields. This dissertation presents a relative definition of measurement of fuzzy degree, inclusion degree and similarity degree to type-2 fuzzy sets, and discusses certain relativity and properties among them. Illustrations for practical demand are used to show how to calculate the measurement of fuzzy degree, inclusion degree and similarity degree among type-2 fuzzy sets. Furthermore, in the discussion, the algorithm of Yang and Shish is used as a method for cluster analysis, and comparison is made with the results of Hung and Yang. According to different α-levels, these cluster results are reasonably included in a hierarchical tree.