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Parameter Estimation of Fuzzy Controller Using Genetic Optimization and Neurofuzzy Networks

  • Sungkwun Oh
  • Seokbeom Roh
  • Taechon Ahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

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.

Keywords

Fuzzy Controller Inverted Pendulum Detailed Comparative Analysis Optimize Control Parameter Inverted Pendulum System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sungkwun Oh
    • 1
  • Seokbeom Roh
    • 2
  • Taechon Ahn
    • 2
  1. 1.Department of Electrical EngineeringThe University of SuwonHwaseong-si, Gyeonggi-doSouth Korea
  2. 2.Department of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea

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