Identification of Hammerstein Type Nonlinear Systems using the Automatic Choosing Function Model and Genetic Algorithm

T. Hachino, K. Deguchi, and H. Takata (Japan)


Identification, Hammerstein system, Genetic algorithm, Automatic choosing function


In this paper an identification method of Hammerstein type nonlinear systems is proposed by using an auto matic choosing function (ACF) model and genetic algo rithm (GA). The data region of the input signals is divided into some subdomains and unknown nonlinear static part to be estimated is approximately represented by a linear lo cal equation on each subdomain. These local equations are united into a single one by the ACF smoothly. The con nection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least squares method. The widths of the subdomains and shape of the ACF are properly determined by using the GA. Sim ulation results are shown to illustrate the propose method.

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