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Chaotic Feature Selection and Reconstruction in Time Series Prediction Read chapter Read first chapter

2016 | OriginalPaper | Chapter

L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction

Authors : Meiling Xu, Min Han, Shunshoku Kanae

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Echo state network contains a randomly connected hidden layer and an adaptable output layer. It can overcome the problems associated with the complex computation and local optima. But there may be ill-posed problem when large reservoir state matrix is used to calculate the output weights by least square estimation. In this study, we use L1/2 regularization to calculate the output weights to get a sparse solution in order to solve the ill-posed problem and improve the generalized performance. In addition, an operation of iterated prediction is conducted to test the effectiveness of the proposed L1/2ESN for capturing the dynamics of the chaotic time series. Experimental results illustrate that the predictor has been designed properly. It outperforms other modified ESN models in both sparsity and accuracy.

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Literature
1.
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)CrossRef
2.
Lukoševičius, M., Jaeger, H., Schrauwen, B.: Reservoir computing trends. KI-Künstliche Intelligenz 26(4), 365–371 (2012)CrossRef
3.
Soh, H., Demiris, Y.: Spatio-temporal learning with the online finite and infinite echo-state gaussian processes. IEEE Trans. Neural Netw. Learn. Syst. 26(3), 522–536 (2015)MathSciNetCrossRef
4.
Yuenyong, S., Nishihara, A.: Evolutionary pre-training for CRJ-type reservoir of echo state networks. Neurocomputing 149, 1324–1329 (2015)CrossRef
5.
Chatzis, S.P., Demiris, Y.: Echo state gaussian process. IEEE Trans. Neural Networks 22(9), 1435–1445 (2011)CrossRef
6.
Reinhart, R.F., Steil, J.J.: Regularization and stability in reservoir networks with output feedback. Neurocomputing 90, 96–105 (2012)CrossRef
7.
Han, M., Ren, W.J., Xu, M.L.: An improved echo state network via L1-norm regularization (in Chinese). Acta Automatica Sin. 40(11), 2428–2435 (2014)MATH
8.
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Royal Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)MathSciNetCrossRefMATH
9.
Xu, Z.B., Chang, X.Y., Xu, F.M., Zhang, H.: L1/2 regularization: a thresholding representation theory and a fast solver. IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012)CrossRef
10.
Liang, Y., Liu, C., Luan, X.Z., Leung, L.S., Chan, T.M., Xu, Z.B., Zhang, H.: Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification. BMC Bioinform. 14(1), 198 (2013)CrossRef
11.
Haykin, S.S.: Neural Networks and Learning Machines, 3rd edn., pp. 711–722. Pearson Education, Prentice Hall, Upper Saddle River (2009)
Metadata
Title
L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction
Authors
Meiling Xu
Min Han
Shunshoku Kanae
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_2

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