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Polynomial-Computable Representation of Neural Networks in Semantic Programming Научная публикация

Журнал J — Multidisciplinary Scientific Journal
ISSN: 2571-8800
Вых. Данные Год: 2023, Том: 6, Номер: 1, Страницы: 48-57 Страниц : 10 DOI: 10.3390/j6010004
Ключевые слова polynomiality; polynomial algorithm; logical programming language; semantic programming; AI; neural networks; machine learning
Авторы Goncharov Sergey 1 , Nechesov Andrey 1
Организации
1 Sobolev Institute of Mathematics

Информация о финансировании (1)

1 Институт математики им. С.Л. Соболева СО РАН FWNF-2022-0011

Реферат: A lot of libraries for neural networks are written for Turing-complete programming languages such as Python, C++, PHP, and Java. However, at the moment, there are no suitable libraries implemented for a p-complete logical programming language L. This paper investigates the issues of polynomial-computable representation neural networks for this language, where the basic elements are hereditarily finite list elements, and programs are defined using special terms and formulas of mathematical logic. Such a representation has been shown to exist for multilayer feedforward fully connected neural networks with sigmoidal activation functions. To prove this fact, special p-iterative terms are constructed that simulate the operation of a neural network. This result plays an important role in the application of the p-complete logical programming language L to artificial intelligence algorithms.
Библиографическая ссылка: Goncharov S. , Nechesov A.
Polynomial-Computable Representation of Neural Networks in Semantic Programming
J — Multidisciplinary Scientific Journal. 2023. V.6. N1. P.48-57. DOI: 10.3390/j6010004 РИНЦ OpenAlex
Даты:
Поступила в редакцию: 17 нояб. 2022 г.
Принята к публикации: 4 янв. 2023 г.
Опубликована в печати: 6 янв. 2023 г.
Опубликована online: 6 янв. 2023 г.
Идентификаторы БД:
РИНЦ: 60898111
OpenAlex: W4313829103
Цитирование в БД:
БД Цитирований
OpenAlex 5
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