System identification using grey-based adaptive particle swarm optimization

A grey-based adaptive particle swarm optimization (PSO) applied to the system identification for a class of nonlinear systems is proposed in this study. In the grey-based adaptive PSO, the inertia weights are dynamically adapted according to the results of grey relational analysis at each generation. Every particle has its own inertia weight which may differ for different particles or different generations. Besides, this study attempts to utilize the grey-based adaptive PSO to identify an unknown system whose structure is assumed to be known in advance. Simulation results are compared with PSO with a linearly varying inertia weight and PSO with adaptive inertia weight to demonstrate the search performance of the grey-based adaptive PSO.