Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems. (English) Zbl 1193.93170

Summary: This paper considers the identification problem for Hammerstein output error moving average (OEMA) systems. An auxiliary model-based recursive extended least-squares (RELS) algorithm and an auxiliary model-based multi-innovation extended least-squares (MI-ELS) algorithm are presented using the multi-innovation identification theory. The basic idea is to express the system output as a linear combination of the parameters by using the key-term separation principle and auxiliary model method. The proposed algorithms can give highly accurate parameter estimates. The simulation results show the effectiveness of the proposed algorithms.


93E10 Estimation and detection in stochastic control theory
62F10 Point estimation
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
Full Text: DOI


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