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.
Highlights
IntroductionInanimate helpers can already speak to us in different voices and recognize our speech and appearance
Modern digital life is unthinkable without artificial intelligence technologies
Researchers have already taught such neurons to organize themselves into artificial neural networks in the middle of the 20th century
Summary
Inanimate helpers can already speak to us in different voices and recognize our speech and appearance. They can advise us on probable good purchases or customize our exercise schedule based on our digitized analyzes. Thanks to robotics, they become completely humanoid entities with their own complex behavior and digital intelligence. Several dozens algorithms for training artificial neural systems have been developed. Each of these algorithms was created individually or became a logical development of an existing one. It is proposed to consider one of such methods in more detail in this work
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