New Methods for Control System Signal Sampling in Neural Networks of Power Facilities

The authors of the paper emphasize that when the Nyquist-Shannon sampling theorem is used in practice, there arise several problems, which can be explained only through the use of new methodologies and mathematical models. The review of the researchers' works, as well as the authors' own practical research in the course of processing the statistical sample, which is described by a wave-like sine-cosine function, leads to the conclusion that it is necessary to take into account optimization criteria for high-tech processes and innovative indicators of building functions for the statistical sample, for example, when signals are transmitted and sampled using neural networks at production facilities. In the practice of economic calculations, for example, when making a graphic presentation of trend lines based on the functions built subject to the sampling conditions by the Nyquist theorem, the authors propose to use new methods for approximating piece linear functions, which allow for achieving a smaller error as compared to standard calculation methods. The work resulted in the creation of a neural network regulation algorithm, which will be trained based on the collected data and adapted to a specific type of a boiler unit. Besides, it was established that the task of neural network algorithms in the program is to find the optimal value of the weight coefficient for each argument of the resulting function to obtain the maximum number of predictions of the flare level and the particle burn-up time, which are consistent with reality. The use of these methods for the first time made it possible to significantly reduce the error, which is confirmed not only by calculations, but also by experimental data.