Polynomial-Computable Representation of Neural Networks in Semantic Programming

J

article

Sergey Goncharov¹  Andrey Nechesov¹ 

[1] Sobolev Institute of Mathematics

关键词: polynomiality;    polynomial algorithm;    logical programming language;    semantic programming;    AI;    neural networks;    machine learning;   

DOI  :  10.3390/j6010004

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY