A method of parallel-hierarchical transformation based on population coding is described and the use of this method for image recognition is considered. This parallel-hierarchical transformation is described as a system model for image recognition. Theoretical information, experimental investigations, and a software realization are presented.
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.
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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