Multi-objective optimization of NARX model for system identification using genetic algorithm

Abstract

The problem of constructing an adequate and parsimonious Nonlinear Autoregressive model process with eXogenous input (NARX) structure for modeling nonlinear dynamic system is studied. NARX has been shown to perform function approximation and represent dynamic systems. The structures are usually guessed or selected in accordance with the designer prior knowledge, however the multiplicity of the model parameters make it troublesome to get an optimum structure. The trial and error approach is not efficient and may not arrive to an optimum structure. An alternative algorithm based on multi-objective optimization algorithm is proposed. The developed model should fulfill two criteria or objectives namely good predictive accuracy and optimum model structure. The result shows that the proposed algorithm is able to correctly identify the simulated examples and adequately model real data structure and based on a set of solutions called the Pareto optimal set, from which the best network is selected.