Fundamentals of optimization of training algorithms for artificial neural networks

Abstract

In the modern IT industry, the basis for the nearest progress is artificial intelligence technologies and, in particular, artificial neuron systems. The so-called neural networks are constantly being improved within the framework of their many learning algorithms for a wide range of tasks. In the paper, a class of approximation problems is distinguished as one of the most common classes of problems in artificial intelligence systems. The aim of the paper is to study the most recommended learning algorithms, select the most optimal one and find ways to improve it according to various characteristics. Several of the most commonly used learning algorithms for approximation are considered. In the course of computational experiments, the most advantageous aspects of all the presented algorithms are revealed. A method is proposed for improving the computational characteristics of the algorithms under study.