Polynomial-Computable Representation of Neural Networks in Semantic Programming Full article
| Journal |
J — Multidisciplinary Scientific Journal
ISSN: 2571-8800 |
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| Output data | Year: 2023, Volume: 6, Number: 1, Pages: 48-57 Pages count : 10 DOI: 10.3390/j6010004 | ||
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| Affiliations |
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Funding (1)
| 1 | Sobolev Institute of Mathematics | FWNF-2022-0011 |
Abstract:
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.
Cite:
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
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
Dates:
| Submitted: | Nov 17, 2022 |
| Accepted: | Jan 4, 2023 |
| Published online: | Jan 6, 2023 |
Identifiers:
| Elibrary: | 60898111 |
| OpenAlex: | W4313829103 |
Citing:
| DB | Citing |
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| OpenAlex | 5 |