Making parametric Hammerstein system identification a linear problem

Zhijun Cai; Er-Wei Bai

doi:10.3182/20110828-6-IT-1002.00090

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Making parametric Hammerstein system identification a linear problem

Journal article

Open access

Peer reviewed

Making parametric Hammerstein system identification a linear problem

Zhijun Cai and Er-Wei Bai

IFAC Proceedings Volumes, Vol.44(1), pp.11165-11170

01/2011

DOI: 10.3182/20110828-6-IT-1002.00090

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Abstract

In this paper, we study identification of parametric Hammerstein systems with FIR linear parts. By a proper normalization and a clever characterization, it is shown that the average squared error cost function for identification can be expressed in terms of the inner product between the true but unknown parameter vector and its estimate. Further the cost function is concave in the inner product and linear in the inner product square. Therefore, identification of parametric Hammerstein systems with FIR linear parts is a globally convergent problem and has one and only one (local and global) minimum. This implies that identification of such systems is a linear problem in terms of the inner product square and any local search based identification algorithm converges globally.

Details

Title: Subtitle: Making parametric Hammerstein system identification a linear problem
Creators: Zhijun Cai
Er-Wei Bai - University of Iowa
Resource Type: Journal article
Publication Details: IFAC Proceedings Volumes, Vol.44(1), pp.11165-11170
DOI: 10.3182/20110828-6-IT-1002.00090
ISSN: 1474-6670
Language: English
Date published: 01/2011
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197163802771

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