Research and development of actual methods , technologies and methods applied to solution of applied machine training problems for predictive protection

Abstract

A study was made of relevant techniques, technologies and techniques used to solve applied problems of machine learning, based on materials from scientific articles in highly rated journals of foreign researchers, analytical and review notes from open sources, as well as technical documentation and press releases of technical and software solutions. The search for new methods of model selection, cross-validation, evolutionary and analytical selection of training algorithms is of both scientific and purely practical interest. The development of machine learning technologies will only accelerate in the near future. Currently, we are witnessing progress in the development of automated search methods for constructing effective learning models for data analysis that are applicable to many practical problems of data mining. During the review of modern trends in machine learning, we identified promising areas of fundamental and applied research in this area. Development of a process model based on the use of neural networks. Neural networks are successfully used for the synthesis of control systems for dynamic objects. Neural networks have a number of properties that determine the prospects of their use as an analytical apparatus of control systems. In the context of the problem under consideration, this is, above all, the ability to learn by example. The presence of large volumes of monitoring data, which presents interconnected measurements of both the inputs and outputs of the studied system, allows the neural network to be provided with representative training samples.