Elimination of Negative Circuits in Certain Neural Network Structures to Achieve Stable Solutions


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The problem of improving structural properties of artificial discrete neural networks is investigated. The structure of the neural network is regarded as a theoretical graph. Cyclical substructures can occur in this structure under certain conditions, e.g., when there is feedback among neural network layers. Some properties of cycles in the graphs corresponding to neural networks have a significant effect not only on the rate of convergence to the (stable) solutions of the problems posed by network users, but also on the very possibility of obtaining these solutions. These properties include the negativity of some circuits (simple cycles) in neural network graphs. We propose an algorithm that makes it possible to eliminate negative circuits from neural network graphs under certain constraints formulated in this paper. It increases the chances of finding correct solutions to the problems for which neural networks were developed. An illustrative example is presented.

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Yu. Karpov

Luxoft Professional LLC

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Email: y.l.karpov@yandex.ru
俄罗斯联邦, 1-i Volokolamskii proezd 10, Moscow, 123060

L. Karpov

Ivannikov Institute for System Programming, Russian Academy of Sciences; Moscow State University

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Email: mak@ispras.ru
俄罗斯联邦, ul. Solzhenitsyna 25, Moscow, 109004; Moscow, 119991

Yu. Smetanin

Federal Research Center Computer Science and Control, Russian Academy of Sciences; Moscow Institute of Physics and Technology

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Email: ysmetanin@rambler.ru
俄罗斯联邦, ul. Vavilova 44/2, Moscow, 119333; Institutskii per. 9, Dolgoprudnyi, Moscow oblast, 141701

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