Parameter identification of Box-Jenkins systems based on the differential evolution algorithm

This paper considers the parameter estimation of the Box-Jenkins system. The differential evolution algorithm is used to identify the parameters of the Box-Jenkins system and it is compared to the improved particle swarm optimization algorithm. Simulation results show that these two algorithms can effectively identify the Box-Jenkins system. The differential evolution algorithm can give more accurate parameter estimation and identification accuracy than the improved particle swarm optimization algorithm.