Identification of nonlinear volterra systems using differential evolution algorithm

In this work, differential evolution algorithm (DEA) has been applied to adaptive identification of nonlinear volterra systems and compared its performance to that of genetic algorithm (GA), clonal selection algorithm (CSA) and recursive least square algorithm (RLS). Parametric nonlinear volterra systems have been identified using these algorithms. The simulation results have shown that nonlinear systems can be identified using DEA with low modeling error.