Parallel-hierarchical network learning methods and their application to pattern recognition

This paper considers provisions necessary for developing parallel-hierarchical network learning methods underlain by the idea of population coding in an artificial neural network and its approximation to natural neural networks. Mathematical parallel-hierarchical network learning models and a combin...

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Bibliographic Details
Published in:Cybernetics and systems analysis 2011-11, Vol.47 (6), p.986-994
Main Authors: Timchenko, L. I., Melnikov, V. V., Kokryatskaya, N. I.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Approximation
Artificial Intelligence
Control
Cybernetics
Learning
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Methods
Networks
Neural networks
Pattern recognition
Processor Architectures
Software Engineering/Programming and Operating Systems
Studies
Systems Theory
Teaching methods
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Summary:This paper considers provisions necessary for developing parallel-hierarchical network learning methods underlain by the idea of population coding in an artificial neural network and its approximation to natural neural networks. Mathematical parallel-hierarchical network learning models and a combined parallel-hierarchical network learning method are developed for recognizing static and dynamic patterns.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-011-9378-3