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Literatura académica sobre el tema "Fuzzy"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Fuzzy"

Zhang, Xiaohong. "On Some Fuzzy Filters in Pseudo-BCIAlgebras." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/718972.

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Some new properties of fuzzy associative filters (also known as fuzzy associative pseudo-filters), fuzzyp-filter (also known as fuzzy pseudo-p-filters), and fuzzya-filter (also known as fuzzy pseudo-a-filters) in pseudo-BCIalgebras are investigated. By these properties, the following important results are proved: (1) a fuzzy filter (also known as fuzzy pseudo-filters) of a pseudo-BCIalgebra is a fuzzy associative filter if and only if it is a fuzzya-filter; (2) a filter (also known as pseudo-filter) of a pseudo-BCIalgebra is associative if and only if it is ana-filter (also call it pseudo-afilter); (3) a fuzzy filter of a pseudo-BCIalgebra is fuzzya-filter if and only if it is both a fuzzyp-filter and a fuzzyq-filter.

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Şengönül, M., and Z. Zararsız. "Some Additions to the Fuzzy Convergent and Fuzzy Bounded Sequence Spaces of Fuzzy Numbers." Abstract and Applied Analysis 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/837584.

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Some properties of the fuzzy convergence and fuzzy boundedness of a sequence of fuzzy numbers were studied in Choi (1996). In this paper, we have consider, some important problems on these spaces and shown that these spaces are fuzzy complete module spaces. Also, the fuzzyα-, fuzzyβ-, and fuzzyγ-duals of the fuzzy module spaces of fuzzy numbers have been computeded, and some matrix transformations are given.

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Mukherjee, M. N., та S. P. Sinha. "Fuzzyθ-closure operator on fuzzy topological spaces". International Journal of Mathematics and Mathematical Sciences 14, № 2 (1991): 309–14. http://dx.doi.org/10.1155/s0161171291000364.

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The paper contains a study of fuzzyθ-closure operator,θ-closures of fuzzy sets in a fuzzy topological space are characterized and some of their properties along with their relation with fuzzyδ-closures are investigated. As applications of these concepts, certain functions as well as some spaces satisfying certain fuzzy separation axioms are characterized in terms of fuzzyθ-closures andδ-closures.

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Kumbhojkar, H. V. "Proper Fuzzification of Prime Ideals of a Hemiring." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/801650.

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Prime fuzzy ideals, prime fuzzyk-ideals, and prime fuzzyh-ideals are roped in one condition. It is shown that this way better fuzzification is achieved. Other major results of the paper are: every fuzzy ideal (resp.,k-ideal,h-ideal) is contained in a prime fuzzy ideal (resp.,k-ideal,h-ideal). Prime radicals and nil radicals of a fuzzy ideal are defined; their relationship is established. The nil radical of a fuzzyk-ideal (resp., anh-ideal) is proved to be a fuzzyk-ideal (resp.,h-ideal). The correspondence theorems for different types of fuzzy ideals of hemirings are established. The concept of primary fuzzy ideal is introduced. Minimum imperative for proper fuzzification is suggested and it is shown that the fuzzifications introduced in this paper are proper fuzzifications.

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Uehara, Kiyohiko, and Kaoru Hirota. "A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 2 (March 20, 2021): 213–25. http://dx.doi.org/10.20965/jaciii.2021.p0213.

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A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.

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Lee, Seok Jong, and Eun Pyo Lee. "Fuzzyr-continuous and fuzzyr-semicontinuous maps." International Journal of Mathematics and Mathematical Sciences 27, no. 1 (2001): 53–63. http://dx.doi.org/10.1155/s0161171201010882.

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We introduce a new notion of fuzzyr-interior which is an extension of Chang's fuzzy interior. Using fuzzyr-interior, we define fuzzyr-semiopen sets and fuzzyr-semicontinuous maps which are generalizations of fuzzy semiopen sets and fuzzy semicontinuous maps in Chang's fuzzy topology, respectively. Some basic properties of fuzzyr-semiopen sets and fuzzyr-semicontinuous maps are investigated.

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M.Thusleem, Furjana, and M.Bhanumathi. "Fuzzy Metric Dimension of Fuzzy Hypercube Qn and Fuzzy Boolean Graphs." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (February 29, 2020): 3690–96. https://doi.org/10.35940/ijeat.C6226.029320.

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Let G = (V, E, ) be a fuzzy graph. Let M be a subset of V. M is said to be a fuzzy metric basis of G if for every pair of vertices x, y  V ‒ M, there exists a vertex w  M such that d (w, x)  d (w, y). The number of elements in M is said to be fuzzy metric dimension (FMD) of G and is denoted by  (G). The elements in M are called as source vertices. In this paper, we study the fuzzy metric dimension of fuzzy hypercube Qn, fuuzy Boolean Graph BG2 (G) and fuzzy Boolean Graph BG3 (G).

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Jeon, Joung Kon, Young Bae Jun, and Jin Han Park. "Intuitionistic fuzzy alpha-continuity and intuitionistic fuzzy precontinuity." International Journal of Mathematics and Mathematical Sciences 2005, no. 19 (2005): 3091–101. http://dx.doi.org/10.1155/ijmms.2005.3091.

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A characterization of intuitionistic fuzzyα-open set is given, and conditions for an IFS to be an intuitionistic fuzzyα-open set are provided. Characterizations of intuitionistic fuzzy precontinuous (resp.,α-continuous) mappings are given.

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Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

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Dughman, Hawwa M. S., Amnnah A. A. Rasheed, and Khadeejah A. A. Alqamoudi. "Pythagorean M-Fuzzy subgroups under a T-norm." International Science and Technology Journal 32, no. 1 (April 1, 2023): 1–17. https://doi.org/10.62341/hakp0275.

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This study explores the structural properties of Pythagorean M- fuzzy groups, building upon prior works by several researchers. It introduces the Pythagorean M-Fuzzy subgroup as a generalization of intuitionistic fuzzy subgroups, along with Pythagorean M-Fuzzy cosets and Pythagorean M-Fuzz normal subgroups. The study also discusses the impact of group homomorphisms on Pythagorean M- Fuzzy subgroups under a T-norm. Keywords: Pythagorean fuzzy set, Pythagorean fuzzy subgroup, M-fuzzy subgroup, Pythagorean M-fuzzy subgroup

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Tesis sobre el tema "Fuzzy"

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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Made available in DSpace on 2015-03-03T15:48:40Z (GMT). No. of bitstreams: 1 IvanM_TESE.pdf: 1798098 bytes, checksum: 03ae24b47cf77f2591da995af74bd268 (MD5) Previous issue date: 2013-12-06 In 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.

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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.

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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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Made available in DSpace on 2014-12-17T14:55:08Z (GMT). No. of bitstreams: 1 ClaudileneGC_TESE.pdf: 853804 bytes, checksum: 011bfb4befb8b54fce2cd4b1b724efdb (MD5) Previous issue date: 2012-08-20 The 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.

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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.

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Gomes, Luciana Takata 1984. "On fuzzy differential equations = Sobre equações diferenciais fuzzy." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307565.

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Orientadores: Laécio Carvalho de Barros, Barnabas Bede Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica Made available in DSpace on 2018-08-24T00:25:40Z (GMT). No. of bitstreams: 1 Gomes_LucianaTakata_D.pdf: 2706174 bytes, checksum: bb5833b7b2c8f5e094b30eefd64e7426 (MD5) Previous issue date: 2014 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.

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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.

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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.

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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.

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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 technique Fingerabdrücke 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.

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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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Más fuentes

Libros sobre el tema "Fuzzy"

Boynton, Sandra. Fuzzy, fuzzy, fuzzy!: Touch, skritch, & tickle book. New York, NY: Little Simon, 2003.

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Siegfried, Gottwald, ed. Fuzzy sets, fuzzy logic, fuzzy methods with applications. Chichester: J. Wiley, 1995.

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Angleberger, Tom. Fuzzy. New York: Abrams, 2016.

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Kahlert, Jörg, and Hubert Frank. Fuzzy-Logik und Fuzzy-Control. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6.

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Kahlert, Jörg, and Hubert Frank. Fuzzy-Logik und Fuzzy-Control. Wiesbaden: Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-83760-8.

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Gottwald, Siegfried. Fuzzy Sets and Fuzzy Logic. Wiesbaden: Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-86812-1.

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Driankov, Dimiter, Peter W. Eklund, and Anca L. Ralescu, eds. Fuzzy Logic and Fuzzy Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7.

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Mordeson, John N., and Premchand S. Nair, eds. Fuzzy Graphs and Fuzzy Hypergraphs. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1854-3.

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Maria, Bojadziev, ed. Fuzzy sets, fuzzy logic, applications. Singapore: World Scientific Pub. Co., 1995.

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1932-, Klir George J., and Yuan Bo, eds. Fuzzy sets, fuzzy logic, and fuzzy systems: Selected papers. Singapore: World Scientific, 1996.

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Más fuentes

Capítulos de libros sobre el tema "Fuzzy"

Rommelfanger, Heinrich. "Fuzzy-Wahrscheinlichkeiten, Fuzzy-Alternativen, Fuzzy-Zustände." In Fuzzy Decision Support-Systeme, 120–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-57929-5_5.

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Rommelfanger, Heinrich. "Fuzzy-Wahrscheinlichkeiten, Fuzzy-Alternativen, Fuzzy-Zustände." In Entscheiden bei Unschärfe, 126–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-97118-1_4.

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Nowé, Ann. "Keep Fuzzy Controllers Fuzzy." In International Series in Intelligent Technologies, 267–89. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2357-4_11.

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Mann, Heinz, Horst Schiffelgen, Rainer Froriep, and Klaus Webers. "Fuzzy-Regler (Fuzzy-Controller)." In Einführung in die Regelungstechnik, 315–39. München: Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446456945.010.

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Potton, Alois. "Fuzzy." In Abgründe der Informatik, 11–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-22975-6_3.

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Bandemer, Hans. "Fuzzy Analysis of Fuzzy Data." In Fuzzy Logic, 385–94. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_36.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy-Set-Theorie." In Fuzzy-Logik und Fuzzy-Control, 5–39. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6_1.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy-PID-Reglerentwurf." In Fuzzy-Logik und Fuzzy-Control, 311–22. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6_10.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy Construction Unit — Demo." In Fuzzy-Logik und Fuzzy-Control, 323–33. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6_11.

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Kahlert, Jörg, and Hubert Frank. "Fuzzy-lnferenz." In Fuzzy-Logik und Fuzzy-Control, 41–82. Wiesbaden: Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-89197-6_2.

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Actas de conferencias sobre el tema "Fuzzy"

Qu, Yanpeng, Jiaxing Wu, Zhanwen Wu, and Longzhi Yang. "Fuzzy Rule Interpolation with A General Representation of Fuzzy Sets." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612091.

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Chakeri, Alireza, Nasser Sadati, and Setareh Sharifian. "Fuzzy Nash equilibrium in fuzzy games using ranking fuzzy numbers." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584733.

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Torra, Vicenc, Laya Aliahmadipour, and Anders Dahlbom. "Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737731.

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Mezzomo, Ivan, Benjamin C. Bedregal, and Regivan H. N. Santiago. "On fuzzy ideals of fuzzy lattice." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251307.

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Gegeny, David, and Szilveszter Kovacs. "Fuzzy Interpolation of Fuzzy Rough Sets." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882837.

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Razak, Tajul Rosli, Nor Hanimah Kamis, Nurul Hanan Anuar, Jonathan M. Garibaldi, and Christian Wagner. "Decomposing Conventional Fuzzy Logic Systems to Hierarchical Fuzzy Systems." In 2023 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2023. http://dx.doi.org/10.1109/fuzz52849.2023.10309727.

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Seki, Hirosato, and Masaharu Mizumoto. "Fuzzy singleton-type SIC fuzzy inference model." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622583.

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Mekki, Ahmed O., Feng Lin, Hao Ying, and Michael J. Simoff. "Fuzzy detectabilities for fuzzy discrete event systems." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015431.

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Pivert, Olivier, Olfa Slama, and Virginie Thion. "Fuzzy quantified queries to fuzzy RDF databases." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015632.

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Klement, Erich Peter, Radko Mesiar, and Andrea Stupnanova. "Picture fuzzy sets and 3-fuzzy sets." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491520.

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Informes sobre el tema "Fuzzy"

Çitil, Hülya. Solutions of Fuzzy Differential Equation with Fuzzy Number Coefficient by Fuzzy Laplace Transform. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, September 2020. http://dx.doi.org/10.7546/crabs.2020.09.01.

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Tailor, Sanjay. Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada310470.

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Kersten, Paul R. Fuzzy Order Statistics and Their Application to Fuzzy Clustering. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada368624.

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Osipov, G. S. Fuzzy elective selection model. Редакция журнала «ОПиПМ», 2019. http://dx.doi.org/10.18411/oppm-2019-26-1.

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d'Haultfoeuille, Xavier, and Clément de Chaisemartin. Fuzzy differences-in-differences. Cemmap, October 2015. http://dx.doi.org/10.1920/wp.cem.2015.6915.

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Atanasso, Krassimir. Elliptic Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2021. http://dx.doi.org/10.7546/crabs.2021.06.02.

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Kersten, P. R. Fuzzy Robust Statistics for Application to the Fuzzy c-Means Clustering Algorithm. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada274719.

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Meitzler, Thomas J., David Bednarz, E. J. Sohn, Kimberly Lane, and Darryl Bryk. Fuzzy Logic Based Image Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada405123.

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Sirainen, T. IMAP4 Extension for Fuzzy Search. RFC Editor, March 2011. http://dx.doi.org/10.17487/rfc6203.

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Kersten, P. R., and S. C. Nardone. Concepts of Fuzzy Model Assessment. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada280641.

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