Neuro-fuzzy network for the design of repair and maintenance bases

The article is dedicated to the problem of improving the technical operation of cars and transport and technological machines (TTM), in particular the design of repair and maintenance bases (RMB) of cars. As it is known, in the technical operation of machines, the most important task was and remains the task of the correct organization and design of RMB. To solve it, a generally accepted technique is used, in which the final result is the calculation of the area of the production building, as well as the area of the zone of posts and various production units. But the solution is quite complex, which is mainly caused by the data uncertainties in the problem. Uncertainty conditions are taken into account by various coefficients, division into categories, recommendations, which does not add accuracy to the solution of the problem. For this reason, the newly created enterprises for maintenance and repair of cars are being finalized during their operation. Intelligent systems and neural networks can be used to make more informed design decisions in problems of this class. Thus, the goal of the research was determined, which was to create a neural network to determine the design area in the production building of the zone of technological posts for repair and maintenance. The results of the work are a developed neural fuzzy network for determining the area of the zone of technological posts for maintenance and repair. For practical use, the results are recommended for the design of the RMB of the car park.