Selection Of Optimized Fuzzy Rules For Process Control Systems: Using Genetic Algorithms

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Selection Of Optimized Fuzzy Rules For Process Control Systems: Using Genetic Algorithms

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dc.contributor.author Katebi, S. D. en_US
dc.date.accessioned 2009-11-25T13:06:24Z
dc.date.available 2009-11-25T13:06:24Z
dc.date.issued 1996 en_US
dc.identifier.citation Engineering Journal of Qatar University, 1996, Vol. 9, Pages 147-169. en_US
dc.identifier.uri http://hdl.handle.net/10576/7978
dc.description.abstract In this paper modeling, selection and optimization of fuzzy rules for multivariable feedback process control and decision making systems are developed and discussed. A general model for multivariable fuzzy rules is derived and sets of fuzzy parameters are assigned to the consequents of each rule. A genetic algorithm is used to optimize these parameters. A genetic algorithm search technique is also developed for the selection of the sub-set of appropriate rules from a larger theoretically possible set. The procedure is implemented on a multivariable dynamic model and results of extensive simulation studies are presented to demonstrate a satisfactory performance of the proposed approach en_US
dc.language.iso en en_US
dc.publisher Qatar University en_US
dc.subject Engineering: Research & Technology en_US
dc.title Selection Of Optimized Fuzzy Rules For Process Control Systems: Using Genetic Algorithms en_US
dc.type Article en_US
dc.identifier.pagination 147-169 en_US
dc.identifier.volume 9 en_US

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