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Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and RecognitionTIMCHENKO, L.   , KOKRIATSKAIA, N. , MELNIKOV, V. , MAKARENKO, R. , PETROVSKYI, N. |
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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
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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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
