Abstract
In this study, we introduce a noble neurogenetic approach to the design of fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). 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 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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© 2005 Springer-Verlag Berlin Heidelberg
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Oh, S., Roh, S., Ahn, T. (2005). Parameter Estimation of Fuzzy Controller Using Genetic Optimization and Neurofuzzy Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_16
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DOI: https://doi.org/10.1007/11427469_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
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