Maximum Likelihood Identification of Dual-rate Hammerstein Systems

Abstract

Aiming at a kind of dual-rate Hammerstein system, based on the auxiliary model identification idea, using the maximum likelihood principle and recursive identification technology, this paper proposes a maximum likelihood recursive least squares algorithm. The main method is to construct an auxiliary model for the unknown output in the model, and use the output of the auxiliary model to predict the unknown output. This method can directly identify parameters based on the dual-rate input and output data. The simulation experiments show that the proposed algorithm can effectively identify the dual-rate Hammerstein system, and the final error tends to be about 1%.

Keywords

• dual-rate system
• hammerstein system
• maximum likelihood identification
• auxiliary model