Robust training of radial basis networks

Robust training of radial-basis networks under non-normally distributed noise is considered. The simulation results show that multistep projection training algorithms minimizing various forms of module criteria are rather efficient in this case.

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Bibliographic Details
Published in:Cybernetics and systems analysis 2011-11, Vol.47 (6), p.863-870
Main Authors: Rudenko, O. G., Bezsonov, O. O.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Computer simulation
Control
Criteria
Cybernetics
Estimates
Mathematics
Mathematics and Statistics
Maximum likelihood method
Networks
Neural networks
Noise
Pattern recognition
Processor Architectures
Projection
Software Engineering/Programming and Operating Systems
Studies
Systems Theory
Training
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Description
Summary:Robust training of radial-basis networks under non-normally distributed noise is considered. The simulation results show that multistep projection training algorithms minimizing various forms of module criteria are rather efficient in this case.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-011-9365-8