Journal article
Making parametric Hammerstein system identification a linear problem
Automatica (Oxford), Vol.47(8), pp.1806-1812
2011
DOI: 10.1016/j.automatica.2011.05.002
Abstract
In this paper, we study the 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, the 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 the 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 CaiEr-Wei Bai
- Resource Type
- Journal article
- Publication Details
- Automatica (Oxford), Vol.47(8), pp.1806-1812
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.automatica.2011.05.002
- ISSN
- 0005-1098
- eISSN
- 1873-2836
- Grant note
- ECS-0555394 / NSF
- Language
- English
- Date published
- 2011
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083899602771
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