Radial Basis Neural Network Learning Based on Particle Swarm Optimization to Multistep Prediction of Chaotic Lorenz's System

Authors:
Fabio A. Guerra;Leandro dos S. Coelho
Affiliations:
ATENA - Intelligent Systems, Curitiba, PR, Brazil;Pontifícal Catholic University of Parana, PUCPR/PPGEPS/LAS,Curitiba, PR, Brazil
Venue:
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Year:
2005

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Abstract

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.