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Nonlinear system identification using particle swarm optimisation tuned radial basis function models

Chen, S., Harris, C. J., Hong, X. ORCID: and Luk, B. L. (2009) Nonlinear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation, 1 (4). pp. 246-257. ISSN 1758-0374

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To link to this item DOI: 10.1504/IJBIC.2009.024723


A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune 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 often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15163
Uncontrolled Keywords:nonlinear systems; orthogonal least squares algorithm; OLS algorithm; leave-one-out cross validation; tunable radial basis function networks; tunable RBF networks; particle swarm optimisation; PSO; modelling; neural networks; bio-inspired computation; system identification.

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