On-line Supervised Adaptive Training Using Radial Basis Function Networks

A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised n...

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Bibliographic Details
Published in:Neural networks 1996-12, Vol.9 (9), p.1597-1617
Main Authors: Fung, Chi F., Billings, Steve A., Luo, Wan
Format: Article
Language:English
Subjects:
Adaptive filtering
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Dynamical system modelling
Exact sciences and technology
Model selection
Neural network learning algorithm
Parameter estimation
Pattern recognition
Radial basis function network
System identification
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Summary:A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised network training. A new concise on-line correlation based performance monitoring scheme is also introduced as an auxiliary device to detect structural changes in temporal data processing applications. Practical and simulated examples are included to demonstrate the effectiveness of the new procedures. Copyright © 1996 Elsevier Science Ltd.
ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(96)00024-X