A tunable radial basis function model for nonlinear system identification using particle swarm optimisationChen, 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. 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: 10.1109/CDC.2009.5399687 Abstract/SummaryA 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.
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