On-Line Supervised Adaptive Training Using Radial Basis Function Networks
Fung, Chi. F., Billings, S.A. and Luo, Wan.
(1994)
On-Line Supervised Adaptive Training Using Radial Basis Function Networks.
Research Report.
ACSE Research Report 554
.
Department of Automatic Control and Systems Engineering
Abstract
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.
Metadata
Authors/Creators:
Fung, Chi. F.
Billings, S.A.
Luo, Wan.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
Radial basis function network, Neural network learning algorithm; Parameter estimation; Adaptive filtering; System identification; Dynamical system modelling; Model selection; Pattern recognition.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)