Multivariate Time Series Prediction Based on a Simple RBF Network

Jian Hui Xi; Hai Dan Wang; Li Ying Jiang

doi:10.4028/www.scientific.net/AMR.566.97

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 566Multivariate Time Series Prediction Based on a...

Multivariate Time Series Prediction Based on a Simple RBF Network

Abstract:

Improvement on defining hidden layer’s clustering centers of RBF network is greatly important for the network modeling and prediction, especially for multivariate time series. This paper firstly uses a linear function and a nonlinear function respectively to detect the linear correlations and the nonlinear correlations of the time series. And a small data set which includes effective information of the system is defined. Then, a local search procedure is introduced to optimize K-means clustering algorithm, which is aimed at quickly and effectively adjusting hidden layer’s clustering centers. Finally, input other training samples to determine network weights, LS or its improved method can be selected. Simulation results show that compared with that of conventional RBF network, with the same number of hidden layer’s nodes, the RBF network model in this paper gets better prediction accuracy.

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Periodical:

Advanced Materials Research (Volume 566)

Pages:

97-102

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.566.97

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Online since:

September 2012

Authors:

Jian Hui Xi, Hai Dan Wang, Li Ying Jiang

Keywords:

Multivariate Series, Prediction, RBF Network

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References

[1] H. Leung, T. Lo, and S. Wang, Prediction of Noisy Chaotic Time Series Using an Optimal Radial Basis Function Neural Network, IEEE Trans. Neural Networks, 12 (2001) 1163-1172.

DOI: 10.1109/72.950144

Google Scholar

[2] F. Guo, S. Wang, and H. Cui, Fault Diagnosis of Aeroengines Based on RBF Network, Aeronautical Computing Technique, 37 (2007) 23-25.

Google Scholar

[3] M. Zhu, D. Zhang, Study on the Algorithms of Selecting the Radial Basis Function Center, Journal of Anhui University (Natural Science Edition), 24 (2000) 72-79.

Google Scholar

[4] J. Xi, M. Han, Prediction of Multivariate Time Series Based on Principal Component Analysis and Neural Networks, Control Theory and Application, 24 (2007) 719-724.

Google Scholar

[5] S. Chen, C. F. N. Cowan, P. M. Grant, Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks, IEEE Trans. Neural Networks, 2 (1991) 302-309.

DOI: 10.1109/72.80341

Google Scholar

[6] J. Zhang, S. Hu, Chaotic Time Series Prediction Based on RBF Neural Networks with a New Clustering Algorithm, Acta Physica Sinica, 56 (2007) 713-719.

DOI: 10.7498/aps.56.713

Google Scholar

[7] J. Wu, W. Zhu, An Iterated Local Search Algorithm for K-Means Clustering, Computer Engineering and Application, 40 (2004) 37-41.

Google Scholar