Please use this identifier to cite or link to this item: https://doi.org/10.3182/20110828-6-IT-1002.00492
Title: A correlation least-squares method for Hammerstein model identification with ARX and μ-Markov structures
Authors: Lum, K.-Y. 
Bernstein, D.S.
Keywords: ARMA models
Least-squares estimation
Markov parameters
Nonlinear models
System identification
Issue Date: 2011
Citation: Lum, K.-Y.,Bernstein, D.S. (2011). A correlation least-squares method for Hammerstein model identification with ARX and μ-Markov structures. IFAC Proceedings Volumes (IFAC-PapersOnline) 18 (PART 1) : 11183-11189. ScholarBank@NUS Repository. https://doi.org/10.3182/20110828-6-IT-1002.00492
Abstract: This paper presents a two-step method for identification of the SISO Hammerstein model, which employs input autocorrelation and input-output cross-correlation functions as data for least-squares estimation. Using separable processes as input signals, the proposed method allows the linear block of a Hammerstein model to be identified up to a multiplicative constant, without a priori knowledge of the nonlinear model structure. Both ARX and μ-Markov structures of the linear block are considered, where the main concern is the accuracy of pole and zero estimates. The correlation least-squares method is compared numerically with a well-known nonlinear least-squares method, which shows that the correlation method is consistently accurate across different nonlinear model structures. © 2011 IFAC.
Source Title: IFAC Proceedings Volumes (IFAC-PapersOnline)
URI: http://scholarbank.nus.edu.sg/handle/10635/111513
ISBN: 9783902661937
ISSN: 14746670
DOI: 10.3182/20110828-6-IT-1002.00492
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