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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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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container_title Cybernetics and systems analysis
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creator Rudenko, O. G.
Bezsonov, O. O.
description 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.
doi_str_mv 10.1007/s10559-011-9365-8
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source ABI/INFORM Global; Springer Nature
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
title Robust training of radial basis networks
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