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A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

Chen, S., Hong, X., Luk, B. L. and Harris, C.J. (2009) A tunable radial basis function model for nonlinear system identification using particle swarm optimisation. In: 48th IEEE Conference on Decision and Control, held jointly with the 28th Chinese Control Conference (CDC/CCC 2009) , Shanghai, China, pp. 6762-6767, https://doi.org/10.1109/CDC.2009.5399687.

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To link to this item DOI: 10.1109/CDC.2009.5399687

Abstract/Summary

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Item Type:Conference or Workshop Item (Paper)
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14986
Uncontrolled Keywords:covariance matrices, identification, nonlinear systems, particle swarm optimisation, radial basis function networks , RBF unit centre vector, diagonal covariance matrix, fixed-node RBF network models, leave-one-out mean square error, nonlinear system identification, orthogonal forward regression model construction, orthogonal least squares algorithm, particle swarm optimisation, the-state-of-the-art local regularisation, tunable radial basis function network model
Publisher:IEEE

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