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A POD-Based Center Selection for RBF Neural Network in Time Series Prediction Problems

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Adaptive and Natural Computing Algorithms (ICANNGA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4432))

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Abstract

Center selection based on proper orthogonal decomposition (POD) is presented to select centers for the radial basis function (RBF) neural network in prediction of nonlinear time series. The proposed method takes advantages of the time-sequence feature in time series data and enables the center selection to be implemented in a parallel manner. Simulations on a benchmark problem and on two predictions of stock prices show that the presented method can be applied effectively to the prediction of nonlinear time series. Besides possessing higher precisions in training and testing, the proposed method has stronger generalization and noise resistance abilities, compared to several other popular center selection methods.

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Author information

Authors and Affiliations

  1. College of Computer Science, Jilin Normal University, Siping 136000, China

    Wenbo Zhang

  2. College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China

    Xinchen Guo, Chaoyong Wang & Chunguo Wu

  3. College of Science, Northeast Dianli University, Jilin 132012, China

    Xinchen Guo

  4. Department of Fundamental Sciences, Jilin Teacher’s Institute of, Engineering and Technology, Changchun 130021, China

    Chaoyong Wang

Authors
  1. Wenbo Zhang
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  2. Xinchen Guo
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  3. Chaoyong Wang
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  4. Chunguo Wu
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Editor information

Bartlomiej Beliczynski Andrzej Dzielinski Marcin Iwanowski Bernardete Ribeiro

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© 2007 Springer Berlin Heidelberg

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Zhang, W., Guo, X., Wang, C., Wu, C. (2007). A POD-Based Center Selection for RBF Neural Network in Time Series Prediction Problems. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_22

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  • DOI: https://doi.org/10.1007/978-3-540-71629-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71590-0

  • Online ISBN: 978-3-540-71629-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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