Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
Chen, S., Wu, Y. and Luk, B.L. (1999) Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Transactions on Neural Networks, 10, (5), 1239-1243.
The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||13 Oct 1999|
|Last Modified:||02 Mar 2012 14:01|
|Contributors:||Chen, S. (Author)
Wu, Y. (Author)
Luk, B.L. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||122|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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