ARTIFICIAL NEURAL NETWORKS: BASIC PRINCIPLES AND POSSIBLE IMPLEMENTATIONS

There are many tasks solved by people that can be partially or completely automated. One of the most prom- ising tools for these purposes are artificial neural networks. Neural networks are a technology at the intersection of many disciplines: physics, mathematics, statistics, computer science and technology. They find application in a wide range of tasks, such as time series analysis, regression analysis, pattern recognition in images, etc. It is im- possible not to note an important feature of neural networks: to learn from data both with the participation of a teacher, and to go through the learning process without a teacher. This article discusses the basic terms and basic principles of the functioning of artificial neural networks. At the beginning, a mathematical model of the operation of an artificial neuron is given. The main constituent elements of an artificial neuron, such as synapses, inputs, axons, etc., are described. Some subtleties in optimization processes are generalized, and the main types of activa- tion functions are given. Examples of software implementations of neural networks are given, specific application cases are considered, their strengths are noted, as well as some limitations. Given the limitations, an alternative technology is presented: hardware implementations of artificial neural networks. A brief description of the use of neural networks in the world is given, after which the classification of hardware implementations is considered. Each class highlights the features of using such technologies, including strengths and weaknesses. At the end of the article, the question of the relevance of the problem of finding an element base for building hardware solutions in the field of artificial neural networks is raised, arguments are given in favor of the development of hardware solutions. It is shown that it is necessary to further develop the element base for the construction of artificial neu- ral networks.