This work analyzes and describes the potential application of artificial neural networks in the field of technical forecasting. Technical science with sufficient accuracy can determine the probabilistic development of the signal, using the concepts of extrapolation.
Optimal is the use of neural networks in the extrapolation of radio signals. The process of modernization and progress in science provokes a person to try to predict the probabilistic development of the future. The purpose of this article is to understand the aspects of neural networks for determining the success of the operation of extrapolation of nondeterministic broadband radio-technical signals on neural networks.
A lot of research has been carried out in these fields, which show that, in general, artificial neural networks are coping well with complex extrapolation, they make a high competition for statistical processing based on probability theory.
The introduction of artificial neural networks today has an intensive development character. Every year this direction becomes more urgent, the need to use artificial intelligence increases, this requires global automation of processes used in society.
Currently, there are many forecasting methods. In this article some of them we will analyze and compare with artificial neural networks. So, for example, we will show that forecasting using the statistical processing method requires a lot of effort, time and money. Unlike neural networks that eliminate the disadvantages of the statistical method and can be automated and integrated into any signal processing and empirical dependencies.
Artificial neural networks are widely used in the extrapolation of radio engineering signals. In this article we will consider the software tools, algorithms and methods of artificial neural networks.