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Hammerstein model identification algorithm using Bezier-Bernstein approximation

Hong, X. and Mitchell, R. J. (2007) Hammerstein model identification algorithm using Bezier-Bernstein approximation. IET Control Theory and Applications, 1 (4). pp. 1149-1159. ISSN 1751-8644

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To link to this item DOI: 10.1049/iet-cta:20060018

Abstract/Summary

A new identification algorithm is introduced for the Hammerstein model consisting of a nonlinear static function followed by a linear dynamical model. The nonlinear static function is characterised by using the Bezier-Bernstein approximation. The identification method is based on a hybrid scheme including the applications of the inverse of de Casteljau's algorithm, the least squares algorithm and the Gauss-Newton algorithm subject to constraints. The related work and the extension of the proposed algorithm to multi-input multi-output systems are discussed. Numerical examples including systems with some hard nonlinearities are used to illustrate the efficacy of the proposed approach through comparisons with other approaches.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15285
Uncontrolled Keywords:ORTHOGONAL LEAST-SQUARES, SYSTEM-IDENTIFICATION, REGRESSION

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