Готові списки джерел за темами / Fuzzy computation

Добірка наукової літератури з теми "Fuzzy computation"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 16 липня 2024

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Статті в журналах з теми "Fuzzy computation"

Wechler, Wolfgang. "R-fuzzy computation." Journal of Mathematical Analysis and Applications 115, no. 1 (April 1986): 225–32. http://dx.doi.org/10.1016/0022-247x(86)90036-3.

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Dubois, Didier, and Henri Prade. "Handbook of Fuzzy Computation,." Fuzzy Sets and Systems 123, no. 3 (November 2001): 397–98. http://dx.doi.org/10.1016/s0165-0114(01)00092-6.

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Gerla, Giangiacomo. "Theory of fuzzy computation." International Journal of General Systems 44, no. 4 (February 19, 2015): 519–21. http://dx.doi.org/10.1080/03081079.2014.1000641.

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Oussalah, M. "Approximated fuzzy LR computation." Information Sciences 153 (July 2003): 155–75. http://dx.doi.org/10.1016/s0020-0255(03)00071-9.

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Ross, Timothy, and Jonathan Lucero. "Handbook of Fuzzy Computation." Pattern Analysis & Applications 4, no. 1 (March 2001): 77. http://dx.doi.org/10.1007/s100440170031.

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LIAU, CHURN JUNG, and BERTRAND I.-PENG LIN. "FUZZY LOGIC WITH EQUALITY." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 02 (June 1988): 351–65. http://dx.doi.org/10.1142/s0218001488000212.

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The concept of fuzzy equality and its related contents to the first order predicate calculus are discussed. It is proved that, in the viewpoint of computational logic, resolution and paramodulation mechanisms are complete and sound for fuzzy logic with equality. Term rewriting system, that is the set of left to right directional equations, provides an essential computational paradigm for word problems in universal algebra. We embody the fuzzy equality to the theory of this computation system and give an algorithmic solution to the word problems in fuzzy algebra.

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D'hooghe, Bart, Jarosław Pykacz, and Roman R. Zapatrin. "Quantum Computation of Fuzzy Numbers." International Journal of Theoretical Physics 43, no. 6 (June 2004): 1423–32. http://dx.doi.org/10.1023/b:ijtp.0000048625.57350.4f.

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Sandoval, Francisco, Zeng-Guang Hou, and Madan M. Gupta. "Fuzzy-neural Computation and Robotics." Soft Computing 11, no. 3 (March 3, 2006): 211. http://dx.doi.org/10.1007/s00500-006-0061-y.

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Wang, Jing-Zhong, Yingchieh Ho, and Tsung-Ying Sun. "Fuzzy Scaled Mutation Evolutionary Computation." International Journal of Fuzzy Systems 18, no. 6 (February 18, 2016): 1162–79. http://dx.doi.org/10.1007/s40815-016-0155-3.

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Sharma, Kapil, Varsha Dixit, Brijesh Kumar Chaurasia, and Shekhar Verma. "Trust computation using fuzzy analyser." International Journal of Information Technology, Communications and Convergence 3, no. 3 (2019): 177. http://dx.doi.org/10.1504/ijitcc.2019.10028182.

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Дисертації з теми "Fuzzy computation"

Galea, Michelle. "Fuzzy rules from ant-inspired computation." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2701.

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This research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets. A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one. FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel. Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved. With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data. In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases.

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Haas, Benjamin D. "Efficient general type-2 fuzzy computation." abstract and full text PDF (UNR users only), 2009. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1464436.

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AMARAL, JOSE FRANCO MACHADO DO. "SYNTHESIS OF FUZZY SYSTEMS THROUGH EVOLUTIONARY COMPUTATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3550@1.

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UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO
Síntese de Sistemas Fuzzy por Computação Evolucionária propõe uma metodologia de projeto para o desenvolvimento de sistemas fuzzy fundamentada em técnicas de computação evolucionária. Esta metodologia contempla as etapas de concepção do sistema fuzzy e a implementação em hardware do circuito eletrônico que o representa. A concepção do sistema é realizada num ambiente de projeto no qual sua base de conhecimento - composta da base de regras e demais parâmetros característicos - é evoluída, por intermédio de simulação, através do emprego de um novo algoritmo de três estágios que utiliza Algoritmos Genéticos. Esta estratégia enfatiza a interpretabilidade e torna a criação do sistema fuzzy mais simples e eficiente para o projetista, especialmente quando comparada com o tradicional ajuste por tentativa e erro. A implementação em hardware do circuito é realizada em plataforma de desenvolvimento baseada em Eletrônica Evolucionária. Um conjunto de circuitos, denominados de blocos funcionais, foi desenvolvido e evoluído com sucesso para viabilizar a construção da estrutura final do sistema fuzzy.
Synthesis of Fuzzy Systems through Evolutionary Computation proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses Genetic Algorithms (GAs) evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design simpler and more efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a Field Programmable Analog Array (FPAA). A set of evolved circuits called functional blocks allows the implementation of the fuzzy system.

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Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.

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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.

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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.

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Bondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 23, 2009) Vita. Includes bibliographical references.

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Silva, Ricardo Coelho. "Programação multi-objetivo fuzzy." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260594.

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Анотація:

Orientadores: Akebo Yamakami, Jose Luis Verdegay Galdeano
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-14T06:44:19Z (GMT). No. of bitstreams: 1 Silva_RicardoCoelho_D.pdf: 1144878 bytes, checksum: 38379443fb6892fd6eda74c55c3b99dc (MD5) Previous issue date: 2009
Resumo: O objetivo deste trabalho é buscar, estudar e estabelecer as condições de otimali-dade para resolver problemas de programação multi-objetivo irrestritos e restritos em um ambiente impreciso. Essas imprecisões estão presentes nos problemas da vida real e existem muitas formas de tratá-las, mas nesse trabalho será usado a teoria de conjuntos nebulosos. Utilizando como base a otimização nebulosa, foram desenvolvidas duas abordagens para resolver problemas multi-objetivo nebulosos. A primeira abordagem transforma um problema nebuloso em um problema clássico paramétrico com um número maior de funções objetivo, a qual é chamada de paramétrica. A segunda abordagem, chamada de possibilística, usa a teoria de possibilidade como um índice de comparação entre números nebulosos com a finalidade de garantir condições de otimalidade em um ambiente nebuloso. Alguns exemplos numéricos são resolvidos usando um algoritmo genético chamado NSGA-II elitista, com algumas modificações para a comparação de números nebulosos, e depois feita uma análise dos resultados encontrados por ambos os enfoques.
Abstract: The main goal of this work is to search, study and present the optimality conditions to solve the unconstraint and constraint multiobjetive programming problems in imprecise environment. These imprécisions can be found in the real-world optimization problems and there are utmost ways for dealing with them, but in this work will be used the theory of fuzzy sets. Using as a basis the fuzzy optimization, two approaches were developed to solve fuzzy multiobjective problems. The first approach transforms a fuzzy problem into a parametric classic multiobjective programming problem with many more objective functions, which is called parametric approach. The second one, called possibilistic, uses the possibility theory as a comparison index between two fuzzy numbers in order to ensure optimality conditions in a fuzzy environment. Some numerical examples are solved by using a genetic algorithm called elitist NSGA-II with some modifications to compare fuzzy numbers, and then the results obtained with both approaches are analysed.
Doutorado
Automação
Doutor em Engenharia Elétrica

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Leite, Leandro da Costa Moraes. "Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy-Genético." Universidade do Estado do Rio de Janeiro, 2009. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=7530.

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Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy- Genético propõe uma metodologia para o desenvolvimento da base de conhecimento de sistemas fuzzy, fundamentada em técnicas de computação evolucionária. Os sistemas fuzzy evoluídos são avaliados segundo dois critérios distintos: desempenho e interpretabilidade. Uma metodologia para a análise de problemas multiobjetivo utilizando a Lógica Fuzzy foi também desenvolvida para esse fim e incorporada ao processo de avaliação dos AGs. Os sistemas fuzzy evoluídos foram avaliados através de simulações computacionais e os resultados obtidos foram comparados com os obtidos por outros métodos em diferentes tipos de aplicações. O uso da metodologia proposta demonstrou que os sistemas fuzzy evoluídos possuem um bom desempenho aliado a uma boa interpretabilidade da sua base de conhecimento, tornando viável a sua utilização no projeto de sistemas reais.
Genetic-Fuzzy Systems Generation and Simplification of a Knowledge Base proposes a methodology to develop a knowledge base for fuzzy systems through the utilization of evolutionary computational techniques. The evolved fuzzy systems are evaluated considering two distinct criteria: performance and interpretability. Another Fuzzy Logic-based methodology for multiobjective problem analysis was also developed in this work and incorporated in GAs fitness evaluation process. The aforementioned systems were analyzed through computational simulations, and the results were compared to those obtained through other methods, in some applications. The proposed methodology demonstrated that the evolved fuzzy systems are capable of not only good performance, but also good interpretation of their knowledge base, thus showing that they can be effectively used in real world projects.

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[Verfasser], Habtamu Gezahegn Tolossa, and Silke [Akademischer Betreuer] Wieprecht. "Sediment transport computation using a data-driven adaptive neuro-fuzzy modelling approach / Habtamu Gezahegn Tolossa. Betreuer: Silke Wieprecht." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1024692574/34.

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Книги з теми "Fuzzy computation"

Pedrycz, Witold, ed. Fuzzy Evolutionary Computation. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4.

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1953-, Pedrycz Witold, ed. Fuzzy evolutionary computation. Boston: Kluwer Academic Publishers, 1997.

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Pedrycz, Witold. Fuzzy Evolutionary Computation. Boston, MA: Springer US, 1997.

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Computation over fuzzy quantities. Boca Raton: CRC Press, 1994.

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Syropoulos, Apostolos. Theory of Fuzzy Computation. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8379-3.

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H, Ruspini Enrique, Bonissone Piero Patrone, and Pedrycz Witold 1953-, eds. Handbook of fuzzy computation. Bristol: Institute of Physics Pub., 1998.

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Furuhashi, Takeshi, and Yoshiki Uchikawa, eds. Fuzzy Logic, Neural Networks, and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7.

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Meng, Hongying, Tao Lei, Maozhen Li, Kenli Li, Ning Xiong, and Lipo Wang, eds. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4.

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Xie, Quan, Liang Zhao, Kenli Li, Anupam Yadav, and Lipo Wang, eds. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0.

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Liu, Yong, Lipo Wang, Liang Zhao, and Zhengtao Yu, eds. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32456-8.

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Частини книг з теми "Fuzzy computation"

Zohuri, Bahman, and Masoud Moghaddam. "Boolean Computation Versus Fuzzy Logic Computation." In Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation, 291–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53417-6_11.

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Sakawa, Masatoshi, and Kazuya Sawada. "Fuzzy 0-1 Programming Through Neural Computation." In Fuzzy Logic, 311–20. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_29.

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Michalewicz, Zbigniew, Robert Hinterding, and Maciej Michalewicz. "Evolutionary Algorithms." In Fuzzy Evolutionary Computation, 3–31. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_1.

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Vukovich, George, and James X. Lee. "Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach." In Fuzzy Evolutionary Computation, 223–48. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_10.

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Magdalena, Luis, and Juan R. Velasco. "Evolutionary Based Learning of Fuzzy Controllers." In Fuzzy Evolutionary Computation, 249–68. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_11.

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Nelles, Oliver. "GA-Based Generation of Fuzzy Rules." In Fuzzy Evolutionary Computation, 269–95. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_12.

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Alander, Jarmo T. "An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic." In Fuzzy Evolutionary Computation, 299–318. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_13.

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Cordon, O., F. Herrera, and M. Lozano. "On the Combination of Fuzzy Logic and Evolutionary Computation: A Short Review and Bibliography." In Fuzzy Evolutionary Computation, 33–56. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_2.

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Lee, Michael A., and Henrik Esbensen. "Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components." In Fuzzy Evolutionary Computation, 57–78. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_3.

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Fukuda, Toshio, Naoyuki Kubota, and Takemasa Arakawa. "GA Algorithms in Intelligent Robots." In Fuzzy Evolutionary Computation, 81–105. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_4.

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Тези доповідей конференцій з теми "Fuzzy computation"

Shilian Han, Xiuzhi Sang, Xinwang Liu, and Yong Qin. "Direct centroid computation of fuzzy numbers." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251303.

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Ballini, Rosangela, and Fernando Gomide. "Recurrent fuzzy neural computation: Modeling, learning and application." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584099.

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Navara, Mirko. "Computation with fuzzy quantities." In 7th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.17.

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Chen, K. C., Thomas C. Chen, and Thomas Hu. "Fuzzy residual statics computation." In SEG Technical Program Expanded Abstracts 1991. Society of Exploration Geophysicists, 1991. http://dx.doi.org/10.1190/1.1889088.

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Ren, Shen, Jiangjun Tang, Michael Barlow, and Hussein A. Abbass. "An interactive evolutionary computation framework controlled via EEG signals." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891689.

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Akbarzadeh, Vahab, Alireza Sadeghian, and Marcus V. dos Santos. "Derivation of relational fuzzy classification rules using evolutionary computation." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630598.

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Uhartegaray, Remi, Laurent D'Orazio, Matthew Damigos, and Eleftherios Kalogeros. "Scalable Computation of Fuzzy Joins Over Large Collections of JSON Data." In 2023 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2023. http://dx.doi.org/10.1109/fuzz52849.2023.10309759.

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Hammell, Robert J., and Timothy P. Hanratty. "Fuzzy-based approaches to human computation for military situational awareness." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015705.

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Hamrawi, Hussam, Simon Coupland, and Robert John. "Parallel computation of type-2 fuzzy sets using alpha-cuts." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251231.

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Szilagyi, Laszlo, David Iclanzan, Sandor M. Szilagyi, D. Dumitrescu, and Beat Hirsbrunner. "A generalized c-means clustering model using optimized via evolutionary computation." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277372.

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Borgwardt, Stefan, Felix Distel, and Rafael Peñaloza. Gödel Description Logics: Decidability in the Absence of the Finitely-Valued Model Property. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.199.

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Анотація:

In the last few years there has been a large effort for analysing the computational properties of reasoning in fuzzy Description Logics. This has led to a number of papers studying the complexity of these logics, depending on their chosen semantics. Surprisingly, despite being arguably the simplest form of fuzzy semantics, not much is known about the complexity of reasoning in fuzzy DLs w.r.t. witnessed models over the Gödel t-norm. We show that in the logic G-IALC, reasoning cannot be restricted to finitely valued models in general. Despite this negative result, we also show that all the standard reasoning problems can be solved in this logic in exponential time, matching the complexity of reasoning in classical ALC.

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