Hammerstein model identification using quantum delta-potential-well-based particle swarm optimisation

摘要

This paper presents a method for the identification of Hammerstein models based on quantum delta-potential-well-based particle swarm optimisation (QDPSO). First, the intermediate linear model was established through converting the non-linear equations of Hammerstein to a class of linear one by the function expansion. Second, training samples for intermediate linear model were obtained by operating measured data synthetically, and coefficients of the intermediate model were obtained by the QDPSO algorithm. Then, through the relations of the coefficients of intermediate model and that of Hammerstein model, the non-linear static part and linear dynamic part were identified simultaneously. Finally, the efficiency of the proposed algorithm was demonstrated by simulation examples.