Abstract
In this study, we introduce a new neurogenetic approach to the design of fuzzy controller. The development process exploits the key technologies of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks. The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based Neuro-Fuzzy networks (NFN). The developed approach is applied to a nonlinear system such as an inverted pendulum where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.
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© 2004 Springer-Verlag Berlin Heidelberg
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Oh, SK., Roh, SB., Lee, DY., Jang, SW. (2004). The Design of Fuzzy Controller by Means of CI Technologies-Based Estimation Technique. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_10
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DOI: https://doi.org/10.1007/978-3-540-28648-6_10
Publisher Name: Springer, Berlin, Heidelberg
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