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

Abstract EN

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.