Open AccessMethod of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition
Author(s) -
Leonid I. Timchenko,
Natalia I. Kokriatskaia,
V. V. Melnikov,
R. V. Makarenko,
N. Petrovskyi
Publication year - 2012
Publication title -
advances in electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 23
eISSN - 1844-7600
pISSN - 1582-7445
DOI - 10.4316/aece.2012.04006
Subject(s) - computer science , training (meteorology) , pattern recognition (psychology) , artificial intelligence , machine learning , meteorology , physics
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