Non-linear system identification using particle swarm optimisation tuned radial basis function models
Chen, Sheng, Hong, Xia, Luk, Bing L. and Harris, Chris J. (2009) Non-linear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation, 1, (4), 246-258.
- Published Version
A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of nonlinear 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.
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||27 Apr 2009 09:50|
|Last Modified:||27 May 2013 01:05|
|Contributors:||Chen, Sheng (Author)
Hong, Xia (Author)
Luk, Bing L. (Author)
Harris, Chris J. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||18|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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