Academic literature on the topic 'Fuzzy computation'
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Journal articles on the topic "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.
Full textDubois, 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.
Full textGerla, 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.
Full textOussalah, M. "Approximated fuzzy LR computation." Information Sciences 153 (July 2003): 155–75. http://dx.doi.org/10.1016/s0020-0255(03)00071-9.
Full textRoss, Timothy, and Jonathan Lucero. "Handbook of Fuzzy Computation." Pattern Analysis & Applications 4, no. 1 (March 2001): 77. http://dx.doi.org/10.1007/s100440170031.
Full textLIAU, 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.
Full textD'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.
Full textSandoval, 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.
Full textWang, 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.
Full textSharma, 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.
Full textDissertations / Theses on the topic "Fuzzy computation"
Galea, Michelle. "Fuzzy rules from ant-inspired computation." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2701.
Full textHaas, 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.
Full textAMARAL, 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.
Full textSíntese 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.
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.
Full textCreaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
Full textMatthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
Full textBondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.
Full textThe 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.
Silva, Ricardo Coelho. "Programação multi-objetivo fuzzy." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260594.
Full textTese (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
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.
Full textGenetic-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.
[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.
Full textBooks on the topic "Fuzzy computation"
Pedrycz, Witold, ed. Fuzzy Evolutionary Computation. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4.
Full text1953-, Pedrycz Witold, ed. Fuzzy evolutionary computation. Boston: Kluwer Academic Publishers, 1997.
Find full textPedrycz, Witold. Fuzzy Evolutionary Computation. Boston, MA: Springer US, 1997.
Find full textComputation over fuzzy quantities. Boca Raton: CRC Press, 1994.
Find full textSyropoulos, Apostolos. Theory of Fuzzy Computation. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8379-3.
Full textH, Ruspini Enrique, Bonissone Piero Patrone, and Pedrycz Witold 1953-, eds. Handbook of fuzzy computation. Bristol: Institute of Physics Pub., 1998.
Find full textFuruhashi, 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.
Full textMeng, 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.
Full textXie, 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.
Full textLiu, 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.
Full textBook chapters on the topic "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.
Full textSakawa, 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.
Full textMichalewicz, 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.
Full textVukovich, 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.
Full textMagdalena, 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.
Full textNelles, 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.
Full textAlander, 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.
Full textCordon, 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.
Full textLee, 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.
Full textFukuda, 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.
Full textConference papers on the topic "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.
Full textBallini, 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.
Full textNavara, 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.
Full textChen, 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.
Full textRen, 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.
Full textAkbarzadeh, 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.
Full textUhartegaray, 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.
Full textHammell, 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.
Full textHamrawi, 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.
Full textSzilagyi, 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.
Full textReports on the topic "Fuzzy computation"
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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