<title>Evolved radial basis function networks for identification and control of dynamical systems</title>

Abstract

This paper presents a new approach to the identification and control of dynamical systems by means of evolved radial basis function neural networks (ERBFNs). Traditionally, radial basis function networks (RBFNs) parameters which are used for identification and control are fixed beforehand by a trial-and-error process. This process consists of finding structural and training parameters. Once these parameters are fixed the only parameters that remain to be determined are network weights. In general, the weights are adjusted using a gradient approach so that network output asymptotically follow the plant output. In this paper a new approach to the selection of structural and training parameters is introduced. A hybrid system is proposed which uses an evolutionary algorithm to select optimum structural parameters and uses the LMS algorithm to adjust network weights. In this context, RBFN parameters such as basis function centers, widths and training parameters are chosen at random and adjusted by an evolutionary algorithm, throughout the identification and control process. Experimental results show that the system is able to effectively identify and control dynamical systems.