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

• Published in: Information sciences Vol. 222; pp. 203 - 212
• Main Authors: Wang, Dongqing, Ding, Feng, Chu, Yanyun
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 10.02.2013
• Subjects: Hammerstein model; Key-term separation principle; Least squares; Output error moving average (OEMA) system; Parameter estimation; Recursive identification; Recursive identification; Parameter estimation; Hammerstein model; Key-term separation principle; Output error moving average (OEMA) system; Least squares
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2012.07.064

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.