Filtering Based Recursive Least Squares Algorithm for Multi‐Input Multioutput Hammerstein Models

Abstract

This paper considers the parameter estimation problem for Hammerstein multi‐input multioutput finite impulse response (FIR‐MA) systems. Filtered by the noise transfer function, the FIR‐MA model is transformed into a controlled autoregressive model. The key‐term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational efficiency compared with the recursive least squares algorithm.