Identification of Nonlinear Systems Using a Model with the Automatic Choosing Function

Determination of the Model Structure by the Genetic Algorithm

Abstract

This paper deals with an identification method based on a model with an automatic choosing function (ACF) for nonlinear systems. A full data region or a whole domain is divided into some subdomains and the unknown nonlinear function to be estimated is approximately described by a linear equation on each subdomain. These linear equations are united into a single one by the ACF smoothly, and thus the resulting model becomes linear in the parameters. Hence these parameters are easily evaluated by the linear least squares method. Moreover the structure of the model by the ACF expansion is properly determined by the genetic algorithm, where the AIC by Akaike is utilized as an objective function. Numerical experiments are carried out to demonstrate the effectiveness of this approach.