Academic literature on the topic 'Optimization of fuzzy systemparameters'

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Journal articles on the topic "Optimization of fuzzy systemparameters"

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NASSERI, S. H. "FUZZY NONLINEAR OPTIMIZATION." Journal of Nonlinear Sciences and Applications 01, no. 04 (2008): 230–35. http://dx.doi.org/10.22436/jnsa.001.04.05.

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ISHII, Hiroaki. "Fuzzy Combinatiorial Optimization." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 31–40. http://dx.doi.org/10.3156/jfuzzy.4.1_31.

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Murat, Y. Sazi, and Shinya Kikuchi. "Fuzzy Optimization Approach." Transportation Research Record: Journal of the Transportation Research Board 2024, no. 1 (2007): 82–91. http://dx.doi.org/10.3141/2024-10.

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Dey, Samir, and Tapan Kumar Roy. "Multi-objective Structural Optimization Using Fuzzy and Intuitionistic Fuzzy Optimization Technique." International Journal of Intelligent Systems and Applications 7, no. 5 (2015): 57–65. http://dx.doi.org/10.5815/ijisa.2015.05.08.

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Ammar, Elsaid, and Joachim Metz. "On fuzzy convexity and parametric fuzzy optimization." Fuzzy Sets and Systems 49, no. 2 (1992): 135–41. http://dx.doi.org/10.1016/0165-0114(92)90319-y.

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Chen, T. Y., and C. C. Shieh. "Fuzzy multiobjective topology optimization." Computers & Structures 78, no. 1-3 (2000): 459–66. http://dx.doi.org/10.1016/s0045-7949(00)00091-2.

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Medaglia, Andrés L., Shu-Cherng Fang, and Henry L. W. Nuttle. "Fuzzy controlled simulation optimization." Fuzzy Sets and Systems 127, no. 1 (2002): 65–84. http://dx.doi.org/10.1016/s0165-0114(01)00153-1.

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Luhandjula, M. K., and M. M. Gupta. "On fuzzy stochastic optimization." Fuzzy Sets and Systems 81, no. 1 (1996): 47–55. http://dx.doi.org/10.1016/0165-0114(95)00240-5.

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Pedrycz, W., and J. V. de Oliveira. "Optimization of fuzzy models." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 4 (1996): 627–36. http://dx.doi.org/10.1109/3477.517038.

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Luhandjula, M. K. "Fuzzy optimization: An appraisal." Fuzzy Sets and Systems 30, no. 3 (1989): 257–82. http://dx.doi.org/10.1016/0165-0114(89)90019-5.

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Dissertations / Theses on the topic "Optimization of fuzzy systemparameters"

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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.

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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
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Tada, Minoru. "STUDIES ON FUZZY COMBINATORIAL OPTIMIZATION." Kyoto University, 1994. http://hdl.handle.net/2433/160752.

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本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである<br>Kyoto University (京都大学)<br>0048<br>新制・論文博士<br>博士(工学)<br>乙第8724号<br>論工博第2927号<br>新制||工||976(附属図書館)<br>UT51-94-Z475<br>(主査)教授 茨木 俊秀, 教授 長谷川 利治, 教授 片山 徹<br>学位規則第4条第2項該当
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Dadone, Paolo. "Design Optimization of Fuzzy Logic Systems." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/27893.

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Fuzzy logic systems are widely used for control, system identification, and pattern recognition problems. In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. In this problem we typically wish to minimize the sum of the squares of errors in approximating the data. We first introduce fuzzy logic systems and the superv
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Hu, Cheng Lin. "Design optimization of fuzzy models in system identification." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2493501.

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Lehar, Matthew A. 1977. "A branching fuzzy-logic classifier for building optimization." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32512.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.<br>Includes bibliographical references (p. 109-110).<br>We present an input-output model that learns to emulate a complex building simulation of high dimensionality. Many multi-dimensional systems are dominated by the behavior of a small number of inputs over a limited range of input variation. Some also exhibit a tendency to respond relatively strongly to certain inputs over small ranges, and to other inputs over very large ranges of input variation. A branching linear discriminant can be used to is
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Arnett, Timothy J. "Iteratively Increasing Complexity During Optimization for Formally Verifiable Fuzzy Systems." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin156387481300899.

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Gasir, Fathi Sidig. "Optimization of fuzzy regression trees using artificial immune systems." Thesis, Manchester Metropolitan University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574508.

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This thesis presents the development of a novel fuzzy regression tree algorithm known as Elgasir, which is based on the CHAID regression tree algorithm and Takagi-Sugeno fuzzy inference. The Elgasir Algorithm is applied to crisp regression trees to produce fuzzy regression trees in order to soften sharp decision boundaries inherited in crisp trees. Elgasir generates a fuzzy rule base by applying fuzzy techniques to crisp regression trees using Trapezoidal membership functions. Then Takagi-Sugeno fuzzy inference is used to aggregate the final output from the fuzzy implications. A novel optimiza
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Bahri, Oumayma. "A fuzzy framework for multi-objective optimization under uncertainty." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10030/document.

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Cette thèse est consacrée à l'étude de l’optimisation combinatoire multi-objective sous incertitudes. Plus particulièrement, nous abordons les problèmes multi-objectifs contenant des données floues qui sont exprimées par des nombres triangulaires floues. Pour faire face à ce type de problèmes, notre idée principale est d’étendre les concepts multi-objectifs classiques au contexte flou. Nous proposons, dans un premier temps, une nouvelle approche Pareto entre des objectifs flous (i.e. vecteurs des nombres triangulaires flous). Ensuite, nous étendons des méta-heuristiques basées sur Pareto afin
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Spence, William G. "An Optimization Approach To Employee Scheduling Using Fuzzy Logic." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/618.

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An Optimization Approach to Employee Scheduling Using Fuzzy Logic William G. Spence Selection of sales employees is critical because the sales employees represent the company’s image, competitive advantage, technology, and values. In many service systems the majority of consumer contact is with the sales department. Since there are different types of customers, scheduling quality salespersons who can adequately help consumers may affect revenue. This thesis proposes a new methodology for the scheduling of employees in a service system. The methodology uses Fuzzy Logic to calculate possible
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Guo, Lizhu. "Key optimization issues for the design of fuzzy-inference system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35169.pdf.

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Books on the topic "Optimization of fuzzy systemparameters"

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Lodwick, Weldon A., and Janusz Kacprzyk, eds. Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2.

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Wang, Shuming, and Junzo Watada. Fuzzy Stochastic Optimization. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5.

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Fedrizzi, Mario, Janusz Kacprzyk, and Marc Roubens, eds. Interactive Fuzzy Optimization. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-45700-5.

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Gupta, Pankaj, Mukesh Kumar Mehlawat, Masahiro Inuiguchi, and Suresh Chandra. Fuzzy Portfolio Optimization. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54652-5.

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Fang, Yong, Kin Keung Lai, and Shouyang Wang. Fuzzy Portfolio Optimization. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77926-1.

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Fedrizzi, Mario. Interactive Fuzzy Optimization. Springer Berlin Heidelberg, 1991.

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1949-, Fedrizzi Mario, Kacprzyk Janusz, and Roubens Marc, eds. Interactive fuzzy optimization. Springer-Verlag, 1991.

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Sakawa, Masatoshi. Fuzzy Sets and Interactive Multiobjective Optimization. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-1633-4.

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Buckley, James J., and Leonard J. Jowers. Monte Carlo Methods in Fuzzy Optimization. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76290-4.

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Verdegay, José-Luis, ed. Fuzzy Sets Based Heuristics for Optimization. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36461-0.

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Book chapters on the topic "Optimization of fuzzy systemparameters"

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Lodwick, Weldon A., and Elizabeth A. Untiedt. "Fuzzy Optimization." In Computational Complexity. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_75.

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Lodwick, Weldon A., and Elizabeth A. Untiedt. "Fuzzy Optimization." In Encyclopedia of Complexity and Systems Science Series. Springer US, 2009. http://dx.doi.org/10.1007/978-1-0716-2628-3_236.

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Lodwick, Weldon A., and Elizabeth A. Untiedt. "Fuzzy Optimization." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_236.

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Buckley, James J., and Esfandiar Eslami. "Fuzzy Optimization." In An Introduction to Fuzzy Logic and Fuzzy Sets. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1799-7_16.

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Carlsson, Christer, and Robert Fullér. "Fuzzy Optimization." In Fuzzy Reasoning in Decision Making and Optimization. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1805-5_4.

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Loucks, Daniel P. "Fuzzy Optimization." In International Series in Operations Research & Management Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93986-1_17.

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Collette, Yann, and Patrick Siarry. "Fuzzy methods." In Multiobjective Optimization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08883-8_4.

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Virant, Jernej. "Fuzzy Functions." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4673-3_3.

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Virant, Jernej. "Fuzzy Algorithms." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4673-3_4.

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Dompere, Kofi Kissi. "Fuzziness, Rationality, Optimality and Equilibrium in Decision and Economic Theories." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_1.

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Conference papers on the topic "Optimization of fuzzy systemparameters"

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Mikhailov, Dmitriy A., and Maxim V. Abramov. "Novel Optimization Methods for Fuzzy Networks." In 2025 XXVIII International Conference on Soft Computing and Measurements (SCM). IEEE, 2025. https://doi.org/10.1109/scm66446.2025.11060426.

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Chen, Xiangdong, and Junwei Li. "Robust Fuzzy Optimization." In 2008 International Conference on Computer Science and Information Technology (ICCSIT). IEEE, 2008. http://dx.doi.org/10.1109/iccsit.2008.114.

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Nagy, Endre. "Fuzzy membership function optimization." In 9TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4765561.

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Fuller, Robert. "Financial applications of fuzzy reasoning and fuzzy optimization." In 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2012. http://dx.doi.org/10.1109/sisy.2012.6339506.

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Shubbar, Adnan W. K., and Saad S. Mahmood. "Unconstrained fuzzy optimization problems with trapezoidal fuzzy numbers." In 2ND INTERNATIONAL CONFERENCE OF MATHEMATICS, APPLIED SCIENCES, INFORMATION AND COMMUNICATION TECHNOLOGY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0165288.

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TANG, YONGCHUAN, TIANMIN HUANG, and WUMING PAN. "GENETIC OPTIMIZATION WITH FUZZY DECODING." In Proceedings of the 4th International FLINS Conference. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812792631_0017.

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Sepulveda, Abdon, and Hector Jensen. "Fuzzy optimization using approximation concepts." In 6th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4182.

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Xu, Gongxian, and Yezhen Liu. "Fuzzy optimization of biochemical systems." In 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603320.

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Yadmellat, P., S. M. A. Salehizadeh, and M. B. Menhaj. "Fuzzy Parameter Particle Swarm Optimization." In 2008 First International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2008. http://dx.doi.org/10.1109/icinis.2008.111.

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Tian, Dong-ping, and Nai-qian Li. "Fuzzy Particle Swarm Optimization Algorithm." In 2009 International Joint Conference on Artificial Intelligence (JCAI). IEEE, 2009. http://dx.doi.org/10.1109/jcai.2009.50.

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Reports on the topic "Optimization of fuzzy systemparameters"

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Smith, James F., Rhyne III, and II Robert D. Fuzzy Logic Resource Management and Coevolutionary Game-based Optimization. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada390559.

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Dimitrov, Evgeni, Hayden Schaeffer, David Wen, Sandra Rankovic, Kizza Nandyose, and Olivier Thonnard. The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567409.

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Osipov, G. S. Optimization model of an inverse multi-stage problem with fuzzy matches. КультИнформПресс, 2018. http://dx.doi.org/10.18411/spbcsa-2018-9.

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PH
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