期刊
INFORMATION SCIENCES
卷 222, 期 -, 页码 203-212出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2012.07.064
关键词
Least squares; Parameter estimation; Recursive identification; Key-term separation principle; Output error moving average (OEMA) system; Hammerstein model
资金
- Shandong Provincial Natural Science Foundation [ZR2010FM024]
- Project of Shandong Province Higher Educational Science and Technology Program [J10LG12]
- China Postdoctoral Science Foundation [20100471493]
- 111 Project [B12018]
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. (C) 2012 Elsevier Inc. All rights reserved.
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