期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2012
卷号：4
期号：5
页码：125
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Fuzzy systems generally works based on expert knowledge base. Fuzzy Expert knowledge base derived fromthe heuristic knowledge of experts or experience operators in the form of fuzzy control rules andmembership functions (MFs). The major difficulties for designing a fuzzy models and controllers areidentify the optimized fuzzy rules and their corresponding shape, type and distribution of MFs. Moreover,the numbers of fuzzy control rules increases exponentially with the number of input output variables relatedto the control system. For this reason it is very difficult and time consuming for an expert to identify thecomplete rule set and shape of MFs for a complex control system having large number of input and outputvariables. In this paper, we propose a method called evolutionary fuzzy system for tuning the parameters offuzzy rules and adjust the shape of MFs through evolutionary algorithms in order to design a suitable andflexible fuzzy models and controller for complex systems. This paper also presents new flexible encodingmethod methods for evolutionary algorithms. In evolutionary fuzzy system, the evolutionary algorithms isadapted in two different ways Firstly, generating the optimal fuzzy rule sets including the number of rulesinside it and secondly, selecting the optimum shape and distribution of MFs for the fuzzy control rules. Inorder to evaluate the validity and performance of the proposed approach we have designed a test strategyfor the modeling and control of nonlinear systems. The simulation results show the effectiveness of ourmethod and give better performance than existing fuzzy expert systems.
关键词：Fuzzy Expert System; Optimization; Evolutionary Algorithms (EAs); Evolutionary Fuzzy System and;Nonlinear System
