Auxiliary model-based recursive least squares algorithm for two-input single-output Hammerstein output-error moving average systems by using the hierarchical identification principle

This article considers the parameter estimation problems of two-input single-output Hammerstein output-error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non-linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model-based forgetting factor recursive least squares algorithm and the auxiliary model-based forgetting factor multi-innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective.