Comparison of Four Neural Network Learning Methods Based on Genetic Algorithm for Non-linear Dynamic Systems Identification

Abstract

Abstract Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic system, using the genetic algorithm (GA) to train the Layer-Recurrent Network (LRN), Focused Time-Delay Neural Network (FTDNN), the Elman Network, and Nonlinear Autoregressive Network with Exogenous inputs (NARX). The simulation results show the generalization ability of the four dynamic neural networks which provide the high precision of model of the nonlinear dynamic system. Also this paper illustrates the advantages and disadvantages of the different dynamic neural networks trained by GA. Keywords: Dynamic Neural Networks, Nonlinear system identification, genetic algorithm.