Parameter estimation for time-varying system based on combinatorial PSO

Abstract

In this paper, a novel Particle Swarm Optimization (PSO) identification algorithm for time-varying systems with a colored noise is presented. Presented criterion function can show not only outside system output error but also inside parameters error in order to explain more difference between actual and estimative system, identification algorithm may consist of many different PSO algorithms that are named the combinatorial PSO. The estimating and tracking of parameters make use of characteristics of different PSO algorithms. The simulation and result show that the identification algorithm for time-varying systems with noise was indeed more efficient and robust in combinatorial PSO comparing with the original particle swarm optimization. Copyright © 2006 International Federation for Information Processing.