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On-line Supervised Adaptive Training Using Radial Basis Function Networks.

Wan Luo1, Steve A. Billings, Chi F. Fung

  • 1University of Newcastle upon Tyne, UK

Neural Networks : the Official Journal of the International Neural Network Society
|December 1, 1996
PubMed
Summary
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A novel recursive supervised training algorithm enhances radial basis neural networks. This adaptive method efficiently trains networks and detects structural changes in temporal data using correlation-based monitoring.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Radial basis neural networks (RBNNs) are powerful function approximators.
  • Efficient and adaptive training algorithms are crucial for RBNN applications.
  • Detecting structural changes in time-series data is a significant challenge.

Purpose of the Study:

  • To develop a new recursive supervised training algorithm for RBNNs.
  • To enhance the efficiency and adaptiveness of RBNN training.
  • To introduce a method for detecting structural changes in temporal data.

Main Methods:

  • A novel recursive supervised training algorithm is derived for RBNNs.
  • The algorithm integrates on-line candidate regressor selection with Givens QR recursive parameter estimation.

Related Experiment Videos

  • An on-line correlation-based performance monitoring scheme is introduced.

    • Main Results:

    • The new algorithm provides efficient adaptive supervised network training.
    • The correlation-based scheme effectively detects structural changes in temporal data.
    • Practical and simulated examples validate the proposed procedures.

    Conclusions:

    • The developed recursive supervised training algorithm offers an efficient approach for RBNN training.
    • The auxiliary monitoring scheme aids in identifying structural shifts in time-series data.
    • The combined procedures demonstrate effectiveness in adaptive and robust network operation.