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An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks.

Guang-Bin Huang1, P Saratchandran, Narasimhan Sundararajan

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. egbhuang@ntu.edu.sg

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 29, 2004
PubMed
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This study introduces a new Growing and Pruning (GAP)-RBF algorithm for radial basis function networks. It efficiently prunes neurons based on their contribution to learning accuracy, reducing network size and training time.

Area of Science:

  • Machine Learning
  • Artificial Neural Networks

Background:

  • Radial basis function (RBF) networks are a class of neural networks used for function approximation.
  • Sequential learning algorithms adjust network parameters incrementally as new data becomes available.
  • Efficient network construction and pruning are crucial for optimizing RBF network performance.

Purpose of the Study:

  • To introduce a novel sequential Growing and Pruning (GAP)-RBF algorithm.
  • To define and utilize the concept of neuron "Significance" for efficient network adaptation.
  • To evaluate the performance of GAP-RBF against established sequential learning algorithms.

Main Methods:

  • Developed a sequential Growing and Pruning (GAP)-RBF algorithm.
  • Defined neuron "Significance" as its averaged contribution to network output.

Related Experiment Videos

  • Employed a piecewise-linear approximation for Gaussian functions to compute significance efficiently.
  • Tested GAP-RBF on artificial and real-world benchmark datasets with uniform and nonuniform distributions.

    • Main Results:

    • GAP-RBF demonstrated comparable generalization performance to existing algorithms.
    • The algorithm achieved a considerably reduced network size.
    • Training time was significantly decreased compared to other methods.
    • Neuron pruning based on "Significance" proved effective in optimizing network structure.

    Conclusions:

    • The GAP-RBF algorithm offers an efficient approach for constructing and optimizing RBF networks.
    • Neuron "Significance" is a valuable metric for guiding sequential network learning and pruning.
    • GAP-RBF presents a promising alternative for applications requiring fast and compact RBF network models.