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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2012 - 6

Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

TIMCHENKO, L. See more information about TIMCHENKO, L. on SCOPUS See more information about TIMCHENKO, L. on IEEExplore See more information about TIMCHENKO, L. on Web of Science, KOKRIATSKAIA, N. See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about  KOKRIATSKAIA, N. on SCOPUS See more information about KOKRIATSKAIA, N. on Web of Science, MELNIKOV, V. See more information about  MELNIKOV, V. on SCOPUS See more information about  MELNIKOV, V. on SCOPUS See more information about MELNIKOV, V. on Web of Science, MAKARENKO, R. See more information about  MAKARENKO, R. on SCOPUS See more information about  MAKARENKO, R. on SCOPUS See more information about MAKARENKO, R. on Web of Science, PETROVSKYI, N. See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on SCOPUS See more information about PETROVSKYI, N. on Web of Science
 
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Download PDF pdficon (718 KB) | Citation | Downloads: 163 | Views: 3,024

Author keywords
parallel-hierarchical network, training, population coding, preparation, face recognition

References keywords
timchenko(6), hierarchical(6), processing(5), recognition(4), parallel(4), neural(4), networks(4), network(4), learning(4), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 39 - 46
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04006
Web of Science Accession Number: 000312128400006
SCOPUS ID: 84872764925

Abstract
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Propositions necessary for development of parallel-hierarchical (PH) network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute) similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.


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