Research the adaptive neural networks using possibility in control systems for cycle chemistry at thermal power plants

Abstract

The study is devoted to the analysis of the learning possibilities of adaptive neural networks in the context of water-chemical regime management systems. During a series of experiments on a laboratory stand with a disturbing effect, transient characteristics describing changes in the controlled value were obtained. The obtained mathematical models using the TunePID program served as the basis for the analysis and research of adaptive neural networks. Adaptive neural networks are a powerful tool capable of learning from the data obtained and dynamically changing their structure and parameters to accurately predict and control the behavior of the system. Unlike classical mathematical models, adaptive neural networks have the ability to process nonlinear dependencies and adapt to changing conditions without the need for manual reconfiguration. The stages of identification of disturbances and adaptation of compensator settings were described and illustrated. The study included an analysis of the work of a neural network with a different number of hidden neurons and various activation functions. The results showed that neural networks trained on data using an adaptive approach are able to predict and control the system with an accuracy comparable to the calculated parameters of the control coefficients calculated manually.