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# Non-linear system identification using particle swarm optimisation tuned radial basis function models

@article{Chen2009NonlinearSI, title={Non-linear system identification using particle swarm optimisation tuned radial basis function models}, author={Sheng Chen and Xia Hong and Bing Lam Luk and Christopher J. Harris}, journal={IJBIC}, year={2009}, volume={1}, pages={246-258} }

- Published 2009 in IJBIC
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… CONTINUE READING

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