Tesi sul tema "Fuzzy"
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1
Mezzomo, Ivan. "On fuzzy ideals and fuzzy filters of fuzzy lattices". Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18692.
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Previous issue date: 2013-12-06In the literature there are several proposals of fuzzi cation of lattices and ideals concepts. Chon in (Korean J. Math 17 (2009), No. 4, 361-374), using the notion of fuzzy order relation de ned by Zadeh, introduced a new notion of fuzzy lattice and studied the level sets of fuzzy lattices, but did not de ne a notion of fuzzy ideals for this type of fuzzy lattice. In this thesis, using the fuzzy lattices de ned by Chon, we de ne fuzzy homomorphism between fuzzy lattices, the operations of product, collapsed sum, lifting, opposite, interval and intuitionistic on bounded fuzzy lattices. They are conceived as extensions of their analogous operations on the classical theory by using this de nition of fuzzy lattices and introduce new results from these operators. In addition, we de ne ideals and lters of fuzzy lattices and concepts in the same way as in their characterization in terms of level and support sets. One of the results found here is the connection among ideals, supports and level sets. The reader will also nd the de nition of some kinds of ideals and lters as well as some results with respect to the intersection among their families. Moreover, we introduce a new notion of fuzzy ideals and fuzzy lters for fuzzy lattices de ned by Chon. We de ne types of fuzzy ideals and fuzzy lters that generalize usual types of ideals and lters of lattices, such as principal ideals, proper ideals, prime ideals and maximal ideals. The main idea is verifying that analogous properties in the classical theory on lattices are maintained in this new theory of fuzzy ideals. We also de ne, a fuzzy homomorphism h from fuzzy lattices L and M and prove some results involving fuzzy homomorphism and fuzzy ideals as if h is a fuzzy monomorphism and the fuzzy image of a fuzzy set ~h(I) is a fuzzy ideal, then I is a fuzzy ideal. Similarly, we prove for proper, prime and maximal fuzzy ideals. Finally, we prove that h is a fuzzy homomorphism from fuzzy lattices L into M if the inverse image of all principal fuzzy ideals of M is a fuzzy ideal of L. Lastly, we introduce the notion of -ideals and - lters of fuzzy lattices and characterize it by using its support and its level set. Moreover, we prove some similar properties in the classical theory of - ideals and - lters, such as, the class of -ideals and - lters are closed under intersection. We also de ne fuzzy -ideals of fuzzy lattices, some properties analogous to the classical theory are also proved and characterize a fuzzy -ideal on operation of product between bounded fuzzy lattices L and M and prove some results.
2
Biba, Vladislav. "Generované fuzzy implikátory ve fuzzy rozhodování". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-233558.
Testo completo
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Costa, Claudilene Gomes da. "Probabilidades imprecisas: intervalar, fuzzy e fuzzy intuicionista". Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15202.
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Previous issue date: 2012-08-20The idea of considering imprecision in probabilities is old, beginning with the Booles George work, who in 1854 wanted to reconcile the classical logic, which allows the modeling of complete ignorance, with probabilities. In 1921, John Maynard Keynes in his book made explicit use of intervals to represent the imprecision in probabilities. But only from the work ofWalley in 1991 that were established principles that should be respected by a probability theory that deals with inaccuracies. With the emergence of the theory of fuzzy sets by Lotfi Zadeh in 1965, there is another way of dealing with uncertainty and imprecision of concepts. Quickly, they began to propose several ways to consider the ideas of Zadeh in probabilities, to deal with inaccuracies, either in the events associated with the probabilities or in the values of probabilities. In particular, James Buckley, from 2003 begins to develop a probability theory in which the fuzzy values of the probabilities are fuzzy numbers. This fuzzy probability, follows analogous principles to Walley imprecise probabilities. On the other hand, the uses of real numbers between 0 and 1 as truth degrees, as originally proposed by Zadeh, has the drawback to use very precise values for dealing with uncertainties (as one can distinguish a fairly element satisfies a property with a 0.423 level of something that meets with grade 0.424?). This motivated the development of several extensions of fuzzy set theory which includes some kind of inaccuracy. This work consider the Krassimir Atanassov extension proposed in 1983, which add an extra degree of uncertainty to model the moment of hesitation to assign the membership degree, and therefore a value indicate the degree to which the object belongs to the set while the other, the degree to which it not belongs to the set. In the Zadeh fuzzy set theory, this non membership degree is, by default, the complement of the membership degree. Thus, in this approach the non-membership degree is somehow independent of the membership degree, and this difference between the non-membership degree and the complement of the membership degree reveals the hesitation at the moment to assign a membership degree. This new extension today is called of Atanassov s intuitionistic fuzzy sets theory. It is worth noting that the term intuitionistic here has no relation to the term intuitionistic as known in the context of intuitionistic logic. In this work, will be developed two proposals for interval probability: the restricted interval probability and the unrestricted interval probability, are also introduced two notions of fuzzy probability: the constrained fuzzy probability and the unconstrained fuzzy probability and will eventually be introduced two notions of intuitionistic fuzzy probability: the restricted intuitionistic fuzzy probability and the unrestricted intuitionistic fuzzy probability
A id?ia de considerar imprecis?o em probabilidades ? antiga, remontando aos trabalhos de George Booles, que em 1854 pretendia conciliar a l?gica cl?ssica, que permite modelar ignor?ncia completa, com probabilidades. Em 1921, John Maynard Keynes em seu livro fez uso expl?cito de intervalos para representar a imprecis?o nas probabilidades. Por?m, apenas a partir dos trabalhos de Walley em 1991 que foram estabelecidos princ?pios que deveriam ser respeitados por uma teoria de probabilidades que lide com imprecis?es. Com o surgimento da teoria dos conjuntos fuzzy em 1965 por Lotfi Zadeh, surge uma outra forma de lidar com incertezas e imprecis?es de conceitos. Rapidamente, come?aram a se propor diversas formas de considerar as id?ias de Zadeh em probabilidades, para lidar com imprecis?es, seja nos eventos associados ?s probabilidades como aos valores das probabilidades. Em particular, James Buckley, a partir de 2003 come?a a desenvolver uma teoria de probabilidade fuzzy em que os valores das probabilidades sejam n?meros fuzzy. Esta probabilidade fuzzy segue princ?pios an?logos ao das probabilidades imprecisas de Walley. Por outro lado, usar como graus de verdade n?meros reais entre 0 e 1, como proposto originalmente por Zadeh, tem o inconveniente de usar valores muito precisos para lidar com incertezas (como algu?m pode diferenciar de forma justa que um elemento satisfaz uma propriedade com um grau 0.423 de algo que satisfaz com grau 0.424?). Isto motivou o surgimento de diversas extens?es da teoria dos conjuntos fuzzy pelo fato de incorporar algum tipo de imprecis?o. Neste trabalho ? considerada a extens?o proposta por Krassimir Atanassov em 1983, que adicionou um grau extra de incerteza para modelar a hesita??o ao momento de se atribuir o grau de pertin?ncia, e portanto, um valor indicaria o grau com o qual o objeto pertence ao conjunto, enquanto o outro, o grau com o qual n?o pertence. Na teoria dos conjuntos fuzzy de Zadeh, esse grau de n?o-pertin?ncia por defeito ? o complemento do grau de pertin?ncia. Assim, nessa abordagem o grau de n?o-pertin?ncia ? de alguma forma independente do grau de pertin?ncia, e nessa diferencia entre essa n?o-pertin?ncia e o complemento do grau de pertin?ncia revela a hesita??o presente ao momento de se atribuir o grau de pertin?ncia. Esta nova extens?o hoje em dia ? chamada de teoria dos conjuntos fuzzy intuicionistas de Atanassov. Vale salientar, que o termo intuicionista aqui n?o tem rela??o com o termo intuicionista como conhecido no contexto de l?gica intuicionista. Neste trabalho ser? desenvolvida duas propostas de probabilidade intervalar: a probabilidade intervalar restrita e a probabilidade intervalar irrestrita; tamb?m ser?o introduzidas duas no??es de probabilidade fuzzy: a probabilidade fuzzy restrita e a probabilidade fuzzy irrestrita e por fim ser?o introduzidas duas no??es de probabilidade fuzzy intuicionista: a probabilidade fuzzy intuicionista restrita e a probabilidade fuzzy intuicionista irrestrita
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Klapil, Ondřej. "Fuzzy systémy s netradičními antecedenty fuzzy pravidel". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220884.
Testo completoAbstract (sommario):
The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.
5
Gomes, Luciana Takata 1984. "On fuzzy differential equations = Sobre equações diferenciais fuzzy". [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307565.
Testo completoAbstract (sommario):
Orientadores: Laécio Carvalho de Barros, Barnabas BedeTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica
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Resumo: A partir da proposta das definições de derivada e integral fuzzy via extensão de Zadeh dos respectivos operadores para funções clássicas, obtemos uma versão do teorema fundamental do cálculo e desenvolvemos uma nova teoria de equações diferenciais fuzzy (EDFs). Diferentemente dos conceitos anteriores de derivadas (Hukuhara e generalizadas) e integrais para funções fuzzy, em que as funções assumem valores em conjuntos fuzzy, a abordagem aqui proposta lida com tubos fuzzy de funções (subconjuntos fuzzy de espaços de funções). Sob condições razoáveis, as novas operações equivalem a diferenciar (ou integrar) as funções clássicas dos níveis. Apresentamos as abordagens anteriores de EDFs mais conhecidas e, para realizar comparações com a nova teoria, calculamos os conjuntos atingíveis fuzzy das soluções. Provamos que algumas soluções da teoria proposta equivalem às via derivada fortemente generalizada. Também demonstramos a equivalência, sob determinadas condições, com as soluções via inclusões diferenciais fuzzy e extensão de Zadeh da solução clássica. Apesar destas duas abordagens não tratarem de EDFs, elas são largamente difundidas por utilizarem derivadas de funções clássicas (de modo similar ao aqui proposto) e de preservarem características das soluções de sistemas dinâmicos clássicos. Esses são fatos vantajosos, pois mostram que a teoria proposta, além de tratar de EDFs, possui propriedades desejáveis das outras duas mencionadas, permitindo a ocorrência de estabilidade e periodicidade de soluções, por exemplo. A teoria é ilustrada através de sua aplicação em modelos biológicos e análise dos resultados
Abstract: From the definition of fuzzy derivative and integral via Zadeh's extension of the derivative and integral for classical functions we obtain a fundamental theorem of calculus and develop a new theory for fuzzy differential equations (FDEs). Different from the previous concepts of fuzzy derivatives (Hukuhara and generalized derivatives) and integrals, defined for fuzzy-set-valued functions, the approach we propose deals with fuzzy bunches of functions (fuzzy subsets of spaces of functions). Under reasonable conditions, the new operations are equivalent to differentiating (or integrating) the classical functions of the levels. We present the most known previous approaches of FDEs. Comparisons with the new theory we propose are carried out calculating fuzzy attainable sets of the solutions. Under certain conditions, the solutions via strongly generalized derivative coincide with solutions using our approach. The same happens with solutions to fuzzy differential inclusions and Zadeh's extension of the crisp solution. Although these two methods do not treat FDEs, they are widespread for making use of classical functions (similarly to what is proposed in this thesis) and for preserving properties of classical dynamical systems. These are advantageous features since it shows that the new theory presents desirable properties of the other two mentioned theories (allowing for instance periodicity and stability of solutions), besides treating FDEs. The theory is illustrated by applying it on biological models and commenting the results
Doutorado
Matematica Aplicada
Doutora em Matemática Aplicada
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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.
Testo completoAbstract (sommario):
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.
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Lee, John Wan Tung. "The discovery of fuzzy rules from fuzzy databases". Thesis, University of Sunderland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298322.
Testo completo
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Hüsselmann, Claus. "Fuzzy-Geschäftsprozessmanagement /". Lohmar ; Köln : Eul, 2003. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=010483351&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Testo completoAbstract (sommario):
Zugl.: Saarbrücken, Universiẗat, Diss., 2003 u.d.T.: Hüsselmann, Claus: Unscharfe Informationen in der Unternehmensmodellierung - formale Integration von Unbestimmtheitsaspekten in die ereignisgesteuerte Geschäftsprozessmodellierung.
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Strobel, Cornelia. "Fuzzy Fingerprinting". Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200500106.
Testo completoAbstract (sommario):
Fingerprints play an important role in biometrics and cryptography. Their creation might be based on one-way hash functions, which should usually also be collision-resistant. But users tend to draw less attention at those fingerprints - so an attacker might hand out a similar fingerprint in order to spoof identity. The main ideas for creating such 'fuzzy fingerprints' and the creation algorithm itself are discussed in this lecture. The demonstration of the tool, that produces fuzzy fingerprints shows the practical background of this techniqueFingerabdrücke besitzen sowohl in der Kryptographie als auch in der Biometrie eine große Bedeutung. In kryptographischen Anwendungen werden diese durch Einweg-Hash-Verfahren erzeugt, die für bestimmte Anwendungen auch kollisionsresitent sein müssen. In der Praxis schenken Benutzer diesen Fingerprints weit weniger Aufmerksamkeit - oft genügt es nur hinreichend ähnliche Fingerprints auszugeben, um die Nutzer zu täuschen Die Kriterien, die dabei erfüllt sein müssen und die Erzeugung dieser "Fuzzy Fingerprints" sind Hauptbestandteil dieses Vortrags. Durch die Demonstration eines Tools im praktischen Einsatz wird dieser abgeschlossen
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Murugan, Anand. "Fuzzy blackholes". Pomona College, 2007. http://ccdl.libraries.claremont.edu/u?/stc,18.
Testo completoAbstract (sommario):
The fuzzball model of a black hole is an attempt to resolve the many paradoxes and puzzles of black hole physics that have revealed themselves over the last century. These badly behaved solutions of general relativity have given physicists one of the few laboratories to test candidate quantum theories of gravity. Though little is known about exactly what lies beyond the event horizon, and what the ultimate fate of matter that falls in to a black hole is, we know a few intriguing and elegant semi-classical results that have kept physicists occupied. Among these are the known black hole entropy and the Hawking radiation process.
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Duarte, Filho Jorge Costa. "Integrais fuzzy". [s.n.], 1988. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306487.
Testo completoAbstract (sommario):
Orientador : Rodney C. BassaneziDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Científica
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Resumo: Não informado
Abstract: Not informed
Mestrado
Mestre em Matemática
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Geronimo, João Roberto 1963. "Medidas fuzzy". [s.n.], 1988. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306469.
Testo completoAbstract (sommario):
Orientador: Rodney Carlos BassaneziDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Científica
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Resumo: Não informado
Abstract: Not informed
Mestrado
Mestre em Matemática Aplicada
13
Ralbovský, Martin. "Fuzzy GUHA". Doctoral thesis, Vysoká škola ekonomická v Praze, 2006. http://www.nusl.cz/ntk/nusl-77047.
Testo completoAbstract (sommario):
The GUHA method is one of the oldest methods of exploratory data analysis, which is regarded as part of the data mining or knowledge discovery in databases (KDD) scienti_c area. Unlike many other methods of data mining, the GUHA method has firm theoretical foundations in logic and statistics. In scope of the method, finding interesting knowledge corresponds to finding special formulas in satisfactory rich logical calculus, which is called observational calculus. The main topic of the thesis is application of the "fuzzy paradigm" to the GUHA method By the term "fuzzy paradigm" we mean approaches that use many-valued membership degrees or truth values, namely fuzzy set theory and fuzzy logic. The thesis does not aim to cover all the aspects of this application, it emphasises mainly on: - Association rules as the most prevalent type of formulas mined by the GUHA method - Usage of fuzzy data - Logical aspects of fuzzy association rules mining - Comparison of the GUHA theory to the mainstream fuzzy association rules - Implementation of the theory using the bit string approach The thesis throughoutly elaborates the theory of fuzzy association rules, both using the theoretical apparatus of fuzzy set theory and fuzzy logic. Fuzzy set theory is used mainly to compare the GUHA method to existing mainstream approaches to formalize fuzzy association rules, which were studied in detail. Fuzzy logic is used to define novel class of logical calculi called logical calculi of fuzzy association rules (LCFAR) for logical representation of fuzzy association rules. The problem of existence of deduction rules in LCFAR is dealt in depth. Suitable part of the proposed theory is implemented in the Ferda system using the bit string approach. In the approach, characteristics of examined objects are represented as strings of bits, which in the crisp case enables efficient computation. In order to maintain this feature also in the fuzzy case, a profound low level testing of data structures and algoritms for fuzzy bit strings have been carried out as a part of the thesis.
14
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.
Testo completo
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Morillas, Gómez Samuel. "Fuzzy metrics and fuzzy logic for colour image filtering". Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1879.
Testo completoAbstract (sommario):
El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido.
En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente.
En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879
Palancia
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García, Z. Yohn E. "Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller". [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.
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GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller". Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
Testo completoAbstract (sommario):
Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention
al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete
rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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Chen, Guiming. "Fuzzy FOIL: A fuzzy logic based inductive logic programming system". Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9621.
Testo completoAbstract (sommario):
In many domains, characterizations of a given attribute are imprecise, uncertain and incomplete in the available learning examples. The definitions of classes may be vague. Learning systems are frequently forced to deal with such uncertainty. Traditional learning systems are designed to work in the domains where imprecision and uncertainty in the data are absent. Those learning systems are limited because of their impossibility to cope with uncertainty--a typical feature of real-world data. In this thesis, we developed a fuzzy learning system which combines inductive learning with a fuzzy approach to solve problems arising in learning tasks in the domains affected by uncertainty and vagueness. Based on Fuzzy Logic, rather than pure First Order Logic used in FOIL, this system extends FOIL with learning fuzzy logic relation from both imprecise examples and background knowledge represented by Fuzzy Prolog. The classification into the positive and negative examples is allowed to be a degree (of positiveness or negativeness) between 0 and 1. The values of a given attribute in examples need not to be the same type. Symbolic and continuous data can exist in the same attribute, allowing for fuzzy unification (inexact matching). An inductive learning problem is formulated as to find a fuzzy logic relation with a degree of truth, in which a fuzzy gain calculation method is used to guide heuristic search. The Fuzzy FOIL's ability of learning the required fuzzy logic relations and dealing with vague data enhances FOIL's usefulness.
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Mehmood, Rashid. "Fuzzy linear programming problems solved with Fuzzy decisive set method". Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1201.
Testo completoAbstract (sommario):
In the thesis, there are two kinds of fuzzy linear programming problems, one of them is a linear programming problem with fuzzy technological coefficients and the second is linear programming problem in which both the right-hand side and the technological coefficients are fuzzy numbers. I solve the fuzzy linear programming problems with fuzzy decisive set method.
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Peña, Reyes Carlos Andrés. "Coevolutionary fuzzy modeling /". [S.l.] : [s.n.], 2002. http://library.epfl.ch/theses/?display=detail&nr=2634.
Testo completo
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Murugan, Anand. "The fuzzy horizon". Pomona College, 2007. http://ccdl.libraries.claremont.edu/u?/stc,24.
Testo completoAbstract (sommario):
The fuzzball model of a black hole is an attempt to resolve the many paradoxes and puzzles of black hole physics that have revealed themselves over the last century. These badly behaved solutions of general relativity have given physicists one of the few laboratories to test candidate quantum theories of gravity. Though little is known about exactly what lies beyond the event horizon, and what the ultimate fate of matter that falls in to a black hole is, we know a few intriguing and elegant semi-classical results that have kept physicists occupied. Among these are the known black hole entropy and the Hawking radiation process.
22
Ruziyeva, Alina. "Fuzzy Bilevel Optimization". Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2013. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-106378.
Testo completoAbstract (sommario):
In the dissertation the solution approaches for different fuzzy optimization problems are presented. The single-level optimization problem with fuzzy objective is solved by its reformulation into a biobjective optimization problem. A special attention is given to the computation of the membership function of the fuzzy solution of the fuzzy optimization problem in the linear case. Necessary and sufficient optimality conditions of the the convex nonlinear fuzzy optimization problem are derived in differentiable and nondifferentiable cases. A fuzzy optimization problem with both fuzzy objectives and constraints is also investigated in the thesis in the linear case. These solution approaches are applied to fuzzy bilevel optimization problems.
In the case of bilevel optimization problem with fuzzy objective functions, two algorithms are presented and compared using an illustrative example. For the case of fuzzy linear bilevel optimization problem with both fuzzy objectives and constraints k-th best algorithm is adopted.
23
Karim, Ehsanul, Sri Phani Venkata Siva Krishna Madani e Feng Yun. "Fuzzy Clustering Analysis". Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2165.
Testo completoAbstract (sommario):
The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.
24
Parker, Jonathon Karl. "Accelerated Fuzzy Clustering". Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4929.
Testo completoAbstract (sommario):
Clustering algorithms are a primary tool in data analysis, facilitating the discovery of groups and structure in unlabeled data. They are used in a wide variety of industries and applications. Despite their ubiquity, clustering algorithms have a flaw: they take an unacceptable amount of time to run as the number of data objects increases. The need to compensate for this flaw has led to the development of a large number of techniques intended to accelerate their performance. This need grows greater every day, as collections of unlabeled data grow larger and larger. How does one increase the speed of a clustering algorithm as the number of data objects increases and at the same time preserve the quality of the results? This question was studied using the Fuzzy c-means clustering algorithm as a baseline. Its performance was compared to the performance of four of its accelerated variants. Four key design principles of accelerated clustering algorithms were identified. Further study and exploration of these principles led to four new and unique contributions to the field of accelerated fuzzy clustering. The first was the identification of a statistical technique that can estimate the minimum amount of data needed to ensure a multinomial, proportional sample. This technique was adapted to work with accelerated clustering algorithms. The second was the development of a stopping criterion for incremental algorithms that minimizes the amount of data required, while maximizing quality. The third and fourth techniques were new ways of combining representative data objects. Five new accelerated algorithms were created to demonstrate the value of these contributions. One additional discovery made during the research was that the key design principles most often improve performance when applied in tandem. This discovery was applied during the creation of the new accelerated algorithms. Experiments show that the new algorithms improve speedup with minimal quality loss, are demonstrably better than related methods and occasionally are an improvement in both speedup and quality over the base algorithm.
25
Shell, Jethro. "Fuzzy transfer learning". Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/8842.
Testo completoAbstract (sommario):
The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data.
26
Flores, Heriberto Eduardo Roman. "Sobre entropias Fuzzy". [s.n.], 1989. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306480.
Testo completoAbstract (sommario):
Orientador: Rodney Carlos BassanezyDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Científica
Made available in DSpace on 2018-07-14T02:03:54Z (GMT). No. of bitstreams: 1 Flores_HeribertoEduardoRoman_M.pdf: 1678168 bytes, checksum: 55907f1ec85bd31fa6cba1db831ae64d (MD5) Previous issue date: 1989
Resumo: Não informado.
Abstract: Not informed.
Mestrado
Doutor em Matemática
27
Costa, Valdigleis da Silva. "Linguagens lineares fuzzy". PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO, 2016. https://repositorio.ufrn.br/jspui/handle/123456789/25642.
Testo completoAbstract (sommario):
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Made available in DSpace on 2018-07-30T22:29:06Z (GMT). No. of bitstreams: 1 ValdigleisDaSilvaCosta_DISSERT.pdf: 14438862 bytes, checksum: 0296c1fddc9f8c42cbaf0336bdad7e55 (MD5) Previous issue date: 2016-08-05
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
As linguagens formais definidas no final da década de 50, tem uma grande importância dentro da ciência da computação, em especial para aplicações em análise léxica e sintática dentro da construção dos compiladores e também em técnicas de inferência gramatical. A hierarquia estendida de Chomsky além de “organizar” as linguagens formais, nos possibilita traçar uma relação entre as classes de linguagens e os formalismos em termos de máquinas de estados (ou autômatos). Entre as classes de linguagens na hierarquia estão as linguagens lineares, para as linguagens desta classe existem no mínimo quatro tipos de “dispositivos”, que computam sobre elas. Entre eles estão os λ-autômatos lineares não-determinísticos propostos por Bedregal. Ao final da década de 60, Lee e Zadeh propuseram as linguagens fuzzy, numa tentativa de diminuir a distância entre as linguagens formais e as linguagens naturais. Por sua vez, Wee e Fu para capturar a noção de incerteza, durante o processo de reconhecimento de cadeias de uma linguagem, introduzem o conceito de autômatos fuzzy. Assim como na teoria clássica, podemos traçar uma relação entre as classes das linguagens fuzzy e os autômatos fuzzy. No entanto, diferente da teoria clássica, até o presente momento não existe nenhum autômato fuzzy concebido diretamente para computar sobre a classe das linguagens lineares fuzzy, isto é, que se relacione com as linguagens lineares fuzzy de forma direta. Portanto, este trabalho se propõe a realizar um estudo sobre a construção de autômatos fuzzy desenvolvidos para reconhecer as linguagens lineares fuzzy. Além disso, dado que dentro do estudo de linguagens formais, a investigação dos operadores de fecho sobre as classes de linguagens é um importante ponto, neste trabalho, iremos também investigar quais dos operadores (união, intersecção, etc) são fechados sobre as classes das linguagens lineares fuzzy.
Formal languages defined in the late 50’s play an important role in computer science, especially for applications in lexical and syntactic analysis in the construction of compilers and also in grammatical inference techniques. The extended Chomsky hierarchy in addition to “organize” formal languages, enables us to draw a relationship between the classes of languages and formalisms in terms of state machines (or automata). Among the languages classes in the hierarchy, one can find the linear languages. For such languages of this class, there are at least four types of “devices” performing computations on them. One can highlight the Nondeterministic Linear Automata, as proposed by Bedregal. In the end of the 60s, Lee and Zadeh proposed fuzzy languages in an attempt to decrease the distance between formal languages and natural languages. In turn, Wee and Fu capture the concept of uncertainty as they introduced the concept of fuzzy automata during the process of recognizing a language, similarly to the classical theory. Therefore, one can trace a relationship between the classes of fuzzy language and fuzzy automata. However, differently from the classical theory, up to now there is no designed fuzzy automata directly to compute on the class of fuzzy linear languages, i.e., relating to fuzzy linear languages directly. Therefore, this work aims to carry out a study on the construction of fuzzy automata developed to recognize the fuzzy linear languages. Furthermore, based on the study of formal languages, the investigation of the closure operators on languages classes is an important point; this work will also investigate which of the operators (union, intersection, etc.) are closed on the classes of fuzzy linear languages.
28
González, Marek. "Fuzzy neuronové sítě". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234941.
Testo completoAbstract (sommario):
This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.
29
Dalecký, Štěpán. "Neuro-fuzzy systémy". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.
Testo completoAbstract (sommario):
The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system. The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.
30
Burton, Michael Howard. "Fuzzy uniform spaces". Thesis, Rhodes University, 1992. http://hdl.handle.net/10962/d1005222.
Testo completoAbstract (sommario):
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.
31
Stahl, Christoph. "Ein stark konsistenter Kleinst-Quadrate-Schätzer in einem linearen Fuzzy-Regressionsmodell mit fuzzy Parametern und fuzzy abhängigen Variablen". [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972312188.
Testo completo
32
Pergl, Miroslav. "Vývojové prostředí pro umělou inteligenci Modul fuzzy čísel". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218054.
Testo completoAbstract (sommario):
Master’s thesis deals with mathematical operation with fuzzy numbers. The first part of the thesis deals with theoretical knowledge of fuzzy arithmetic and defines fuzzy sets, fuzzy numbers, universum and five membership function used in program. In the concrete it describes – cut method for dealing with fuzzy numbers as with limited interval for specific level which simplifies computation. The second part of the thesis contains description of programmed module for mathematical operation with fuzzy numbers. There is described creation of user interface which is using to set parameters of computation. There are also described support functions which make operation with fuzzy numbers possible and operation ensures output.
33
Cerami, Marco. "Fuzzy Description Logics from a Mathematical Fuzzy Logic point of view". Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/113374.
Testo completoAbstract (sommario):
Description Logic is a formalism that is widely used in the framework of Knowledge Representation and Reasoning in Artificial Intelligence. They are based on Classical Logic in order to guarantee the correctness of the inferences on the required reasoning tasks. It is indeed a fragment of First Order Predicate Logic whose language is strictly related to the one of Modal Logic. Fuzzy Description Logic is the generalization of the classical Description Logic framework thought for reasoning with vague concepts that often arise in practical applications.
Fuzzy Description Logic has been investigated since the last decade of the 20th century. During the first fifteen years of investigation their semantics has been based on Fuzzy Set Theory. A semantics based on Fuzzy Set Theory, however, has been shown to have some counter-intuitive behavior, due to the fact that the truth function for the implication used is not the residuum of the truth function for the conjunction. In the meanwhile, Fuzzy Logic has been given a formal framework based on Many-valued Logic. This framework, called Mathematical Fuzzy Logic, has been proposed has the kernel of a mathematically well founded Fuzzy Logic.
In this dissertation we propose a Fuzzy Description Logic whose semantics is based on Mathematical Fuzzy Logic as its mathematically well settled kernel. To this end we provide a novel notation that is strictly related to the notation that is used in Mathematical Fuzzy Logic. After having settled the notation, we investigate the hierarchies of description languages over different-“t” norm based semantics and the reductions that can be performed between reasoning tasks. The new framework that we establish gives us the possibility to systematically investigate the relation of Fuzzy Description Logic to Fuzzy First Order Logic and Fuzzy Modal Logic. Next we provide some (un)decidability results for the case of infinite “t”-norm based semantics with or without knowledge bases. Finally we investigate the complexity bounds of reasoning tasks without knowledge bases for basic Fuzzy Description Logics over finite “t”-norms.El trabajo desarrollado en esta tesis es una propuesta de sistematizar la formalización de las Lógicas de la Descripción Fuzzy a partir de la Lógica Difusa Matemática. Para ello se define un lenguaje para las Lógicas de la Descripción Fuzzy que extiende el lenguaje de la primera tradición de esta disciplina para adaptarlo al lenguaje más propio de la Lógica Difusa Matemática. Desde el punto de vista semántico, la teoría de conjuntos borrosos cede el paso a una semántica algebraica, que es la que se utiliza en la Lógica Difusa Matemática y que resuelve las consecuencias poco intuitivas que tenía la semántica tradicional. A partir de esta formalización, se tratan temas que eran tradicionales en las Lógicas de la Descripción clásicas como son las jerarquías de inclusiones entre lenguajes de la descripción y la relación de las Lógicas de la Descripción Fuzzy con la Lógica Difusa de primer orden por un lado y la Lógica Difusa Multi-modal por el otro. En relación a problemas de decidibilidad se demuestra que la satisfacción y la subsunción de conceptos en el lenguaje ALE bajo una semántica basada en la Lógica del Producto son problemas decidibles. También se demuestra que la consistencia de bases de conocimiento en el lenguaje ALC bajo una semántica basada en la Lógica de Lukasiewicz es un problema indecidible. En relación a problemas de complejidad computacional se demuestra que satisfacción y validez de fórmulas en la Lógica Modal minimal de Lukasiewicz con valores finitos son problemas PSPACE-completos. También se demuestra que la satisfacción y subsunción de conceptos en el lenguaje IALCED bajo una semántica basada en cualquier lógica difusa con valores finitos son problemas PSPACE-completos. Otra contribución de nuestro trabajo es el estudio sistemático de algoritmos de decisión para la satisfacción y subsunción de conceptos en el lenguaje IALCED, respecto a modelos “witnessed", basados en una reducción de es- tos problemas a los problemas de satisfacción y consecuencia en la lógica proposicional correspondiente.
34
Liu, Da-Wei, e 劉大緯. "Fuzzy Clustering with Fuzzy Data". Thesis, 1997. http://ndltd.ncl.edu.tw/handle/86106062732395692984.
Testo completoAbstract (sommario):
碩士國立清華大學
工業工程研究所
85
In this thesis , we proposed a method to solve a general clustering problem of which the data is fuzzy. There are two major parts in this thesis: one is model-development and the other is a practical application. Regarding the model-development, we extended the Bi-Objective Fuzzy C-means method to the one that can classify fuzzy data by the interval of any h-cut. When we use the proposed method, we not only have the most homogeneous classification(when h=l), but also have different clusterings from different h values. As for the practical applications , we have to classify ten potential services provided by Broad ban information network into three clusters, so that these services in the same cluster can be developed simultaneously. Besides, if we have known the actual cluster in which all the crisp data could belong to, then we can fuzzify these crisp data. If the results of classifying these fuzzified data at certain h-level is the same as that of the original crisp data, then once a collected datum falls in this h-level, we can identify its belonged cluster.
35
XIAO, WEN-ZHONG, e 蕭文忠. "Using fuzzy implication and fuzzy reasoning to construct a fuzzy model". Thesis, 1993. http://ndltd.ncl.edu.tw/handle/20779058889686674201.
Testo completo
36
"Fuzzy semigroups and fuzzy implicative algebra". 2004. http://library.cuhk.edu.hk/record=b6073743.
Testo completoAbstract (sommario):
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.
37
蔡俊傑. "Linear fuzzy maps and fuzzy digraphs". Thesis, 1987. http://ndltd.ncl.edu.tw/handle/15021080986973247962.
Testo completo
38
陳俊男. "Fuzzy classification of interval fuzzy set". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/06405059327459082910.
Testo completo
39
Chen, Ze-jin, e 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.
Testo completoAbstract (sommario):
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.
40
Chou, Ming-Tao, e 周明道. "Fuzzy forecasting based on fuzzy time series". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/06057519378920930426.
Testo completoAbstract (sommario):
博士國立臺灣海洋大學
航運管理學系
92
Abstract Reviewing literatures in fuzzy time series published so far, we find that none of them provide the definition of lower and upper bound of the universe under consideration. Therefore, in this thesis, we propose a method to determine the lower and upper bound of the universe and show that under circumstances where the universe is determined by our method, the forecasting will be better. Taking the instance of the export TEU in Keelung as a benchmark, we compare traditional Season Autoregressive Integrated Moving-Average (SARIMA) with fuzzy time series. Fuzzy time series’ RMSPE which is 0.795% and RMSPE of SARIMA(1,0,1)(0,1,1)12 is 1.038%. That is the estimated result of fuzzy time series is better than traditional SARIMA. Finally, this thesis predicts that total container traffics in Taiwan to justify the applicability of our method. Key words: Time series, Fuzzy time series, The universe of discourse.
41
林小惠. "Fuzzy Clustering Algorithm on Intuition Fuzzy Relations". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/50295746461402621793.
Testo completoAbstract (sommario):
碩士國立新竹師範學院
數理研究所
91
Tamura et al. (1978) constructed an n-step procedure using max-min composition of fuzzy relations and extended to all types of max-t compositions. Yang and Shih (2001) proposed an n-step procedure using max-Δcomposition and proved that max-Δcompositions is better. Here, the n-step procedure is extended to max-t and min-s compositions from Tamura’s (1978) and Yang’s (2001) n-step procedures and it is established on Atanassov’s (1989) point of view－intuitionistic fuzzy sets. Then a clustering algorithm on intuition fuzzy relation is created for the max-t and min-s similarity-relation matrix. A max-Δand min-Δsimilarity- relation matrix with transitivity is obtained by beginning with a proximity-relation matrix based on the n-stpe procedure.
42
Hung, Kuo-Chen, e 洪國禎. "alpha Cut Fuzzy Arithmetic Simplifying Rules, Fuzzy Weighted Average and Fuzzy Function Optimization". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/36542834489043779403.
Testo completoAbstract (sommario):
博士東海大學
工業工程與經營資訊學系
94
The fuzzy theory has been used to solve various problems in management science and engineering. Hence, it becomes important to use fuzzy arithmetic operations. The main purpose of this dissertation is to investigate the procedures of fuzzy arithmetic operation. Furthermore, it provides simplifying rules of fuzzy arithmetic to save required time of arithmetic operation efficiently. We divided this article into three parts to investigate: simplifying rules, fuzzy weighted average (FWA) and fuzzy function optimization. First, the problems of Alpha-cut fuzzy arithmetic have been shown, like in interval arithmetic, that distinct states of fuzzy parameters (or fuzzy variable values) may be chosen and produce an overestimated fuzziness. Meanwhile, local extrema of a function may exist inside the support of fuzzy parameters and cause an underestimation of fuzziness and an illegal fuzzy number’s result. Previously approaches to overcoming these problems have appeared in the literature. Yet, the computational burden of these approaches got even heavier. Thus, this article is based on the vertex method in the literature and extensively proposes newly devised rules observed greatly useful for simplifying the vertex method. These rules are devised through a function partitioned into sub-functions, distinguishing the types of fuzzy parameter/variable occurrences, and types of sub-functions or functions with the various observations. The improved efficiency has been found able to significantly reduce the combination (vertex) test of the vertex method for the fuzzy parameters’ Alpha cut endpoints possibly to only a few fuzzy parameters’ endpoint combinations. Moreover, fuzzy weighted average as function of fuzzy numbers, is suitable for the problem of multiple occurrences of fuzzy parameters. We have reviewed and compared discrete algorithms for the FWAs in both theoretical comparison and numerical comparisons. An alternative efficient algorithm is also proposed. The algorithm introduces an all-candidate (criteria ratings) weights-replaced benchmark adjusting procedure other than a binary (dichotomy) search in the existing methods. In the number of element comparisons, Lee and Park’s algorithm is shown numerically generally slightly better than the alternative algorithm due to the simple binary search scheme used. However, from criterion of average CPU time and average number of evaluations, the alternative algorithm is efficiently, the results outperform than other FWA methods. It has been demonstrated efficient by the proposed alterative algorithm. Finally, a procedure for the fuzzy optimization of fuzzy functions with a fuzzy blurred argument (a single decision variable) is examined base on the -cut arithmetic and the vertex method. When a variable appeared a local solution problem, it becomes important to adjust between function and variable. In this article, a proper and useful preliminary algorithm is proposed. Numerical examples with results are also provided.
43
Lu, Ten lin, e 呂天齡. "A study of fuzzy modeling identification apporach based on fuzzy reasoning and fuzzy cluster". Thesis, 1996. http://ndltd.ncl.edu.tw/handle/83037759907896641744.
Testo completoAbstract (sommario):
碩士國立臺灣師範大學
工業教育學系
84
The purpose of this study was to design a fuzzy modeling identification approach which was based on fuzzy reasoning and fuzzy cluster and to improve the defects of traditional fuzzy modeling identification approach. In this study, first applying the fuzzy reasoning concepts induced the fuzzy model and made the base of a fuzzy modeling identification. Then applying a hierarchical fuzzy clustering approach to classify the output data, it could identify the consequent structure of the fuzzy model and set the initial value of membership function. Next, by means of a double hierarchical objective function fuzzy clustering identified the significant input variables and fuzzy rules of fuzzy modeling. Eventually, this study tried to illustrate some numerical examples to verify the validity of those proposed method.The results of this study were as follows:1. By reviewing literature understood the type of the fuzzy reasoning and the fuzzy cluster and the fuzzy modeling identification approach. According to the direction of this study adopt proper method to achieve the purpose of this study.2. This study applied a hierarchical fuzzy clustering approach to classify the output data, and obtained the initial value of membership function and reduced parameter tuning times. Next in order to reduced times of a structure of fuzzy modeling explored and reduced complexity of fuzzy model structure, and improved defects of traditional fuzzy modeling identification approach by a double hierarchical objective function fuzzy clustering that identify significant input variables and fuzzy rules of fuzzy modeling.3. Applying the results of computer simulation verified that a hierarchical fuzzy clustering approach could link different classification data of single- input single-output system into one system. And a double hierarchical objective function fuzzy clustering and fuzzy curve could rapidly identify the input variables of fuzzy modeling for the multi-input one-output system, and reduced rule base and saved inference times. Therefore the results of this study was verified useful.
44
Aghakhani, Sara. "Neuro-fuzzy architecture based on complex fuzzy logic". 2010. http://hdl.handle.net/10048/891.
Testo completoAbstract (sommario):
Thesis (M.Sc.)--University of Alberta, 2010.Title from PDF file main screen (viewed on May 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering and Intelligent Systems, Department of Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.
45
Jin, Weiqing. "Fuzzy classification based on fuzzy association rule mining". 2004. http://www.lib.ncsu.edu/theses/available/etd-12072004-130619/unrestricted/etd.pdf.
Testo completo
46
Sara, Aghakhani. "Neuro-fuzzy architectures based on complex fuzzy logic". Master's thesis, 2010. http://hdl.handle.net/10048/891.
Testo completoAbstract (sommario):
Complex fuzzy logic is a new type of multi-valued logic, in which truth values are drawn from the unit disc of the complex plane; it is thus a generalization of the familiar infinite-valued fuzzy logic. At the present time, all published research on complex fuzzy logic is theoretical in nature, with no practical applications demonstrated. The utility of complex fuzzy logic is thus still very debatable. In this thesis, the performance of ANCFIS is evaluated. ANCFIS is the first machine learning architecture to fully implement the ideas of complex fuzzy logic, and was designed to solve the important machine-learning problem of time-series forecasting. We then explore extensions to the ANCFIS architecture. The basic ANCFIS system uses batch (offline) learning, and was restricted to univariate time series prediction. We have developed both an online version of the univariate ANCFIS system, and a multivariate extension to the batch ANCFIS system.Software Engineering and Intelligent Systems
47
Lin, Yu-Cheng, e 林育正. "A fuzzy classifier on fuzzy partially-ordered sets". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/71353875118057599416.
Testo completoAbstract (sommario):
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.
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Chang, Tsu-Hao, e 張資昊. "Fuzzy Functional Dependency on Distributed Fuzzy Relational Databases". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/48695606219006732611.
Testo completoAbstract (sommario):
碩士元智大學
資訊管理研究所
91
Fuzzy functional dependency is the basis of normalization and lossless decomposition. Furthermore, it can be used to derive the range of the missing value, detect the feature attribute value in data mining, and decide the contradictory fragment in the selection of materialized view. The data integration is the prerequisite for the issue of fuzzy functional dependency in distributed fuzzy databases. In the context of classical databases, the data integrations of databases have been studied well. However none of them consider the data integration of Fuzzy Relational Databases. This paper first discusses the difficulty of the data integration in fuzzy databases compare to the classical databases, and analyzes the heterogeneity of various fuzzy data models. Second, the methods to integrate the models have been proposed. Finally, the method of combining relations as well as searching fuzzy functional dependencies (ffds) in the distributed fuzzy databases will be presented. The result of this research will contribute towards the advance of the theory in Fuzzy Databases.
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Liao, Wen-Du, e 廖文督. "Fuzzy Portfolio Analysis with FuzzyReturns and Fuzzy InvestmentProportion". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/53063367115813079879.
Testo completoAbstract (sommario):
碩士淡江大學
管理科學研究所碩士班
99
In this paper, the fuzzy portfolio will be discussed due to uncertainty of proportion invested in each selected security in a portfolio. The paper will discuss how to solve the portfolio problem about investment proportion of each selected security based on possibilistic mean-standard deviation models. Then, the uncertain investment proportion of each chosen security in the portfolio will be regarded as a fuzzy number and also be formulated and proposed in this paper, showing how the portfolio selection problem will be solved. Finally, a numerical example of a portfolio selection problem will be shown to illustrate how to deal with it by the mean and the approach the paper presents.
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Lee, Ta-Yang, e 李大仰. "Lossless Decomposition of Fuzzy Relations on Fuzzy Databases". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/56464025959715673191.
Testo completoAbstract (sommario):
碩士元智大學
資訊管理研究所
92
Normalization of relations on relational databases can avoid the redundancies of data and update anomalies, and it emphasizes the lossless decomposition of relations. The outcome of decomposition depends on the definition of the redundancies of tuples. In Extended Possibility-Based fuzzy data model, if the resemblance between the tuples exceeds the threshold, we can say that they are redundant tuples, but the relation of the resemblance doesn’t have the transitivity, so that there is no way to divide the tuples into non overlapping groups, and to merge the tuples in the same group. But, in the Similarity-Based fuzzy data model, the resemblance relation of redundant tuples has transitivity, and when merging the tuples, there is no previous problem. This study just discussed how to use of the definitions of calculating the resemblance between tuples and the definitions of fuzzy functional dependencies up to now, and then find out a way to lossless decompose the relations in Extended- Possibility and Similarity-Based fuzzy data model.