SIMULATION OF A TRAINED TRAINED NEURAL NETWORK OF A FUZZY LOGIC REGULATION SYSTEM BASED ON THE COTTON DRYING PROCESS

The article discusses the modeling of a fuzzy-logical system of regulation of the process of drying of raw cotton. The tasks of overcoming uncertainties arising in the process of operation of technological units at the enterprises of the cotton-cleaning industry are presented. An example of solving such a problem by using an artificial neural network is given. Mathematical models based on the neural network have been developed that are used to formalize the process of drying raw cotton and determine the optimal tuned parameters of the fuzzy-logical PID controller, allowing the fate of changing the operating modes of the technological units of the drying drum. A method for determining the number of synoptic weights of artificial neural networks is proposed, which minimizes the number of trainings and increases the speed of management decisions. To train the neural network weights use the reverse spreading error method. The range of variation of the regulator parameter is justified, taking into account the features of the cotton drying process. As a result, the proposed model was used in the control system of the drying process in terms of quality indicators, which led to an increase in the accuracy of the technological process