DOI: https://doi.org/10.15588/1607-3274-2014-2-15

ARCHITECTURE AND ALGORITHMS OF NEURAL NETWORKS HAMMING AND HEBB, CAPABLE LEARN AND IDENTIFY NEW INFORMATION

V. D. Dmitrienko, A. Yu. Zakovorotniy

Abstract


The problem of the classical discrete neural networks Hamming and Hebb lossless previously stored information additional training. The object of research is the process of recognition and classification of images on systems that are based on artificial neural networks. The subject of research is the architecture and algorithms of artificial neural networks. Objective: To develop a stable and plastic neural networks Hamming and Hebb. The architecture and algorithms of discrete stable and plastic neural networks Hamming and Hebb, which not only can be trained during functioning, but also to recognize the new information. New networks can be an alternative to discrete
neural network adaptive resonance theory. The developed approach for training can be generalized to other neural networks. Experimental investigations of the functioning of the developed algorithms of artificial neural networks. The experimental results confirm the validity of the proposed approach.


Keywords


recognition and classification of images, stable and plastic neural networks, Hamming neural network, Hebb neural network, adaptive resonance theory.

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