Fractional Wiener System Identification Using Heuristic Optimization Technique Based on Key Term Principle

Abstract

In this work, identification of fractional Wiener system is considered; this nonlinear class is defined by the separation of the nonlinear static behavior and the linear invariant time behavior into different blocks. For this purpose, the parameters of the linear and the nonlinear part of the Wiener system as well as the fractional order are estimated, using the heuristic particle swarm optimization (PSO). It is combined with the key term principle to determine the unmeasurable internal variable. In order to confirm the performance of this approach, a simulation example is considered for different signal to noise ratios.