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A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation.

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 Neural Networks
|March 1, 2005
PubMed
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A new generalized growing and pruning algorithm for radial basis function (RBF) networks, GGAP-RBF, creates parsimonious networks. This algorithm enhances learning speed, network size, and generalization performance across various data sampling densities.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Radial basis function (RBF) networks are widely used for function approximation.
  • Existing sequential learning algorithms can be inefficient in terms of network size and learning speed.
  • The need for parsimonious and efficient RBF networks remains a key challenge.

Purpose of the Study:

  • To introduce a novel sequential learning algorithm for RBF networks called the generalized growing and pruning algorithm for RBF (GGAP-RBF).
  • To develop a method for creating parsimonious RBF networks by introducing the concept of neuron significance.
  • To evaluate the performance of GGAP-RBF against other sequential learning algorithms.

Main Methods:

  • The GGAP-RBF algorithm introduces neuron significance, defined as the average information content of a neuron.

Related Experiment Videos

  • A growing and pruning strategy is employed, linking learning accuracy with neuron significance.
  • The algorithm is derived from a rigorous statistical perspective and handles arbitrary sampling densities.

    • Main Results:

    • GGAP-RBF demonstrates superior performance compared to other sequential learning algorithms in benchmark function approximation tasks.
    • The algorithm achieves better learning speed, smaller network sizes, and improved generalization performance.
    • Performance improvements are consistent regardless of the training data's sampling density function.

    Conclusions:

    • GGAP-RBF offers an effective approach for building parsimonious and high-performing RBF networks.
    • The concept of neuron significance is crucial for achieving efficient network construction.
    • The algorithm shows significant advantages for function approximation tasks with varying data distributions.