Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization

  • Sultan Noman Qasem
  • Siti Mariyam Shamsuddin
Conference paper

DOI: 10.1007/978-3-642-01513-7_3

Volume 5553 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Qasem S.N., Shamsuddin S.M. (2009) Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization. In: Yu W., He H., Zhang N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg

Abstract

This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections of weights between the hidden layer and the output layer. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation on dataset illustrate the effectiveness of PSO in enhancing RBF Network learning.

Keywords

RBF Network Hybrid learning Particle Swarm Optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sultan Noman Qasem
    • 1
  • Siti Mariyam Shamsuddin
    • 1
  1. 1.Soft Computing Group, Faculty of Computer Science and Information SystemUTMMalaysia