Accessibility navigation


PSO assisted NURB Neural Network Identification

Hong, X. and Chen, S. (2012) PSO assisted NURB Neural Network Identification. In: ICIC 2012: the Eighth International Conference on Intelligent Computing, 25-29 July 2012, Huangshan, China.

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.

Official URL: http://dx.doi.org/10.1007/978-3-642-31588-6_1

Abstract/Summary

A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm 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 are utilized to demonstrate the efficacy of the proposed approach.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:28183
Uncontrolled Keywords:B-spline NURB neural networks De Boor algorithm Hammerstein model pole assignment controller particle swarm optimization system identification
Additional Information:Intelligent Computing Technology Lecture Notes in Computer Science Volume 7389, 2012, pp 1-9

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

Page navigation