Building models of technological processes based on neuro-fuzzy technology

The work considers the issues of formalization of the extraction process in the form of a generalized regression neural network model, which are the basis for solving the problem of analysis and synthesis of the extraction process control system for obtaining petroleum products. An adaptive learning algorithm for a neural network model has been developed that is characterized by high speed and accuracy. A comparative analysis of the developed model with existing ones was made, which showed the effectiveness of the proposed algorithm for building the architecture of neural network models and learning the weight coefficients of the model.