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The Design of Fuzzy Controller Based on Genetic Optimization and Neurofuzzy Networks
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 Title & Authors
The Design of Fuzzy Controller Based on Genetic Optimization and Neurofuzzy Networks
Oh, Sung-Kwun; Roh, Seok-Beom;
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 Abstract
In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.
 Keywords
Fuzzy controller;Estimation algorithm;Scaling factors;Genetic optimization;FS-based neurofuzzy networks (NFN);FR-based NFN;
 Language
English
 Cited by
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Analysis of Dynamic Model and Design of Optimized Fuzzy PID Controller for Constant Pressure Control, The Transactions of The Korean Institute of Electrical Engineers, 2012, 61, 2, 303  crossref(new windwow)
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Simple Fuzzy PID Controllers for DC-DC Converters, Journal of Electrical Engineering and Technology, 2012, 7, 5, 724  crossref(new windwow)
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