Accessibility navigation


Hammerstein model identification algorithm using Bezier-Bernstein approximation

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 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

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

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:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15285
Uncontrolled Keywords:ORTHOGONAL LEAST-SQUARES, SYSTEM-IDENTIFICATION, REGRESSION

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation