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The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

Hong, X. and Chen, S. (2012) The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization. Neurocomputing, 82. pp. 216-223. ISSN 0925-2312

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To link to this article DOI: 10.1016/j.neucom.2011.11.016

Abstract/Summary

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:26288
Uncontrolled Keywords:B-spline; NURB neural networks; De Boor algorithm; Hammerstein model; Pole assignment controller; Particle swarm optimization; System identification
Publisher:Elsevier

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