Identification of Nonlinear Multivariable Systems by Adaptive Fuzzy Takagi-sugeno Model
نویسندگان
چکیده
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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