Please use this identifier to cite or link to this item:
|Title:||A correlation least-squares method for Hammerstein model identification with ARX and μ-Markov structures||Authors:||Lum, K.-Y.
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 19, 2019
checked on Sep 20, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.