Fifth International Conference on Hybrid Intelligent Systems (HIS'05) Radial Basis Neural Network Learning Based on Particle Swarm Optimization to Multistep Prediction of Chaotic Lorenz?s System Rio de Janeiro, Brazil December 06-December 09 ISBN: 0-7695-2457-5
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.91
This paper presents a hybrid training approach to radial basis function neural networks (RBF-NN). It uses clustering methods to tune the centers of the Gaussian functions used in the hidden layer of a RBF-NN. It also uses particle swarm optimization for centers and spread tuning and the Penrose-Moore pseudo-inverse to adjust the weight's output of the network. Simulations involving this RBF-NN design to identify the chaotic Lorenz? system indicate that the performance of proposed method is better that conventional RBF-NN trained for k-means for multi-step-ahead forecasting.
Citation:
Fabio A. Guerra, Leandro dos S. Coelho, "Radial Basis Neural Network Learning Based on Particle Swarm Optimization to Multistep Prediction of Chaotic Lorenz?s System," his, pp.521-523, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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