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A hybrid MPSO-BP structure adaptive algorithm for RBFNs

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Abstract

This paper introduces a novel hybrid algorithm to determine the parameters of radial basis function neural networks (number of neurons, centers, width and weights) automatically. The hybrid algorithm combines the mix encoding particle swarm optimization algorithm with the back propagation (BP) algorithm to form a hybrid learning algorithm (MPSO-BP) for training Radial Basis Function Networks (RBFNs), which adapts to the network structure and updates its weights by choosing a special fitness function. The proposed method is used to deal with three nonlinear problems, and the results obtained are compared with existent bibliography, showing an improvement over the published methods.

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Acknowledgments

This research was fully supported by National Natural Science Foundation Grant No. 70573101 of the People’s Republic of China.

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, 430074, Wuhan, Hubei, People’s Republic of China

    Shiwei Yu, Kejun Zhu & Siwei Gao

Authors
  1. Shiwei Yu
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  2. Kejun Zhu
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  3. Siwei Gao
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Correspondence to Shiwei Yu.

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Yu, S., Zhu, K. & Gao, S. A hybrid MPSO-BP structure adaptive algorithm for RBFNs. Neural Comput & Applic 18, 769–779 (2009). https://doi.org/10.1007/s00521-008-0214-2

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  • DOI: https://doi.org/10.1007/s00521-008-0214-2

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