Abstract
A method is described for using Radial Basis Function (RBF) network to predict the chaos time series. The structure of RBF network is introduced first, then the two-step training procedure for the network is proposed, that is the unsupervised learning in first layer using K-means clustering algorithm and the supervised learning in second layer using gradient-based methods. Results obtained for a chaos logistic map time series analysis is presented to test the effectiveness of the proposed method. This approach is shown to improve the overall reliability of chaos time prediction.
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References
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Krzy, B., Linder, T.: Radial Basis Function Networks and Complexity Regularization in Function Learning. IEEE Trans. on Neural Networks 9, 247–256 (1998)
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© 2004 Springer-Verlag Berlin Heidelberg
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Chi, W., Zhou, B., Shi, A., Cai, F., Zhang, Y. (2004). Radial Basis Function Network for Chaos Series Prediction. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_146
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DOI: https://doi.org/10.1007/978-3-540-28648-6_146
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
eBook Packages: Springer Book Archive