Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle

This paper concerns parameter identification of Hammerstein output error moving average systems with a two-segment piecewise nonlinearity. By combining the key-term separation principle and the data filtering technique, we transfer the Hammerstein model into two regression identification models, and present a data filtering based recursive least squares method to estimate the parameters of these two identification models. The proposed algorithm achieves a higher computational efficiency than the standard approach by using covariance matrices of smaller dimensions from the two identification models instead of one identification model in the standard approach.