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A sequential learning scheme for function approximation using minimal radial basis function neural networks

Y Lu1, N Sundararajan, P Saratchandran

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Neural Computation
|February 15, 1997
PubMed
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This study introduces a novel sequential learning algorithm for radial basis function neural networks (RBFNNs). It efficiently creates minimal RBFNNs for function approximation and time-series prediction with high accuracy.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Radial Basis Function Neural Networks (RBFNNs) are effective for function approximation and time-series prediction.
  • Existing methods like Resource-Allocating Networks (RAN) can lead to complex network structures.
  • There is a need for algorithms that create minimal yet accurate RBFNNs.

Purpose of the Study:

  • To develop a sequential learning algorithm for constructing minimal RBFNNs.
  • To improve efficiency and reduce computational complexity in RBFNNs.
  • To enhance function approximation and time-series prediction capabilities.

Main Methods:

  • A novel algorithm combining Platt's RAN growth criterion with a hidden unit pruning strategy.
  • The pruning is based on the contribution of each hidden unit to the network output.

Related Experiment Videos

  • Performance evaluation on benchmark datasets: PROBEN1 (hearta), Hermite polynomial, and Mackey-Glass chaotic time series.

    • Main Results:

    • The proposed algorithm successfully generated RBFNNs with significantly fewer hidden neurons compared to existing methods.
    • Achieved comparable or superior accuracy in function approximation and time-series prediction tasks.
    • Demonstrated the ability to create minimal network topologies.

    Conclusions:

    • The developed algorithm effectively produces minimal RBFNNs.
    • It offers an efficient approach for function approximation and time-series prediction.
    • This method provides a balance between network simplicity and predictive performance.