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An efficient learning algorithm for improving generalization performance of radial basis function neural networks.

Z O Wang1, T Zhu

  • 1Institute of Systems Engineering, Tianjin University, People's Republic of China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 18, 2000
PubMed
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This study introduces an efficient recursive learning algorithm that combines Rival Penalized Competitive Learning (RPCL) and Regularized Least Squares (RLS) to enhance Radial Basis Function (RBF) neural network generalization.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Radial Basis Function (RBF) neural networks are powerful function approximators.
  • Improving the generalization performance and computational efficiency of RBF networks remains a key research challenge.
  • Existing methods often struggle with selecting optimal network structures and estimating weights efficiently.

Purpose of the Study:

  • To present an efficient recursive learning algorithm for RBF neural networks.
  • To improve the generalization performance and real-time capability of RBF networks.
  • To develop a minimal RBF network construction procedure.

Main Methods:

  • Combines Rival Penalized Competitive Learning (RPCL) for hidden unit selection and center adjustment.

Related Experiment Videos

  • Utilizes Regularized Least Squares (RLS) for parsimonious network construction and weight estimation.
  • Derives a simple recursive algorithm within RLS, eliminating matrix calculations to reduce computational cost.

    • Main Results:

    • The combined algorithm significantly enhances the generalization performance of RBF networks.
    • The recursive RLS algorithm substantially reduces computational complexity.
    • Simulations on three problems show superior generalization compared to existing algorithms.

    Conclusions:

    • The proposed algorithm offers an efficient and powerful method for constructing minimal RBF networks.
    • The enhanced generalization and real-time capabilities make it suitable for practical applications.
    • This approach effectively addresses limitations in current RBF network training methods.