Prediction of chaotic time series using L-GEM based RBFNN

The prediction of chaotic time series is a vital problem in nonlinear dynamical system. Radial Basis Function Neural Network (RBFNN) has been widely adopted in nonlinear dynamical system identification because of its simple topological structure, fast learning and strong extrapolating capability. Th...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1172 - 1177
Main Authors Hai-Lan Ding, Yeung, D.S., Qian-Li Ma, Ng, W.W.Y., Dong-Liang Wu, Jin-Cheng Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Autoregressive processes
Chaos
Chaotic communication
Chaotic time series prediction
Cybernetics
Localized Generalization Error Model
Machine learning
Neural networks
Neurons
Nonlinear dynamical systems
Prediction methods
Predictive models
RBFNN
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Summary:The prediction of chaotic time series is a vital problem in nonlinear dynamical system. Radial Basis Function Neural Network (RBFNN) has been widely adopted in nonlinear dynamical system identification because of its simple topological structure, fast learning and strong extrapolating capability. The major problem in applying RBFNN is the selection of the number of hidden neurons. In this paper, we adopt the Localized Generalization Error Model (L-GEM) to select number of hidden neurons of RBFNN for chaotic time series prediction. The effectiveness of the L-GEM is evaluated by using two benchmarking chaotic time series datasets: Mackey-Glass series and Lorenz series. Simulations results show that the proposed method provides a better prediction performance in comparison with the RBFNN trained with a cross validation method.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212381