Identification of nonlinear multivariable systems by adaptive fuzzy Takagi-sugeno model, International journal of computational cognition (2004) [2 citations — 0 self]
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by Amine Trabelsi, Frederic Lafont, Mohamed Kamoun, Gilles Enea
International Journal of Computational Cognition (http://www.YangSky.com/yangijcc.htm
http://www.yangsky.com/ijcc238.pdf
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Abstract:
Abstract. This paper investigates the use of a fuzzy method as a tool for model identification of a non linear and multivariable system when the measurement data is available. In fact, the use of fuzzy clustering facilitates automatic generation of Takagi-Sugeno rules and its antecedent parameters. After the determination of the consequent parameters, these are adapted by a recursive least squares algorithm with a forgetting factor in order to use the established model in an adaptive control scheme. Copyright c○2003 Yang’s Scientific Research Institute, LLC. All rights reserved.
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