Polynomial-Computable Representation of Neural Networks in Semantic Programming
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| Název: | Polynomial-Computable Representation of Neural Networks in Semantic Programming |
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| Autoři: | Sergey Goncharov, Andrey Nechesov |
| Zdroj: | J, Vol 6, Iss 1, Pp 48-57 (2023) Volume 6 Issue 1 Pages: 48-57 |
| Informace o vydavateli: | MDPI AG, 2023. |
| Rok vydání: | 2023 |
| Témata: | polynomiality, machine learning, semantic programming, AI, Science, logical programming language, polynomial algorithm, neural networks |
| Popis: | 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. |
| Druh dokumentu: | Article Other literature type |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 2571-8800 |
| DOI: | 10.3390/j6010004 |
| Přístupová URL adresa: | https://doaj.org/article/a1a34c2bf92f4ef9b4d228bc0af62f25 |
| Rights: | CC BY |
| Přístupové číslo: | edsair.doi.dedup.....9653f45b8f51e91118325c1bcc7d047e |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 25718800 |
| DOI: | 10.3390/j6010004 |