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Polynomial-Computable Representation of Neural Networks in Semantic Programming

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Titel: Polynomial-Computable Representation of Neural Networks in Semantic Programming
Autoren: Sergey Goncharov, Andrey Nechesov
Quelle: J, Vol 6, Iss 1, Pp 48-57 (2023)
Volume 6
Issue 1
Pages: 48-57
Verlagsinformationen: MDPI AG, 2023.
Publikationsjahr: 2023
Schlagwörter: polynomiality, machine learning, semantic programming, AI, Science, logical programming language, polynomial algorithm, neural networks
Beschreibung: 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.
Publikationsart: Article
Other literature type
Dateibeschreibung: application/pdf
Sprache: English
ISSN: 2571-8800
DOI: 10.3390/j6010004
Zugangs-URL: https://doaj.org/article/a1a34c2bf92f4ef9b4d228bc0af62f25
Rights: CC BY
Dokumentencode: edsair.doi.dedup.....9653f45b8f51e91118325c1bcc7d047e
Datenbank: OpenAIRE
Beschreibung
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.
ISSN:25718800
DOI:10.3390/j6010004