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Radial basis neural network learning based on particle swarm optimization to multistep prediction of chaotic Lorenz's system

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2 Author(s)
Guerra, F.A. ; ATENA-Intelligent Syst., Curitiba, Brazil ; Coelho, L.D.S.

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 than conventional RBF-NN trained for k-means for multi-step-ahead forecasting.

Published in:

Hybrid Intelligent Systems, 2005. HIS '05. Fifth International Conference on

Date of Conference:

6-9 Nov. 2005