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

Karpov, Y.L.; Karpov, L.E.; Smetanin, Y.G.

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 20 декабря 2019 г.

Авторы: Karpov Yu L., Karpov L.E., Smetanin Yu G.
Журнал: Programming and Computer Software
Том: 45
Номер: 5
Год издания: 2019
Издательство: Pleiades Publishing, Ltd
Местоположение издательства: Road Town, United Kingdom
Первая страница: 241
Последняя страница: 250
DOI: 10.1134/S0361768819050025
Аннотация: 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.
Добавил в систему: Карпов Леонид Евгеньевич

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