Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions

Application of neural network technologies is becoming more extensive every year, especially in the economic field. New methods are being discovered, new models of neural networks are being created. Currently, the main application of neural networks in the economy is the prediction of markets, optimization of commodity-money flows, analysis and generalization of various social surveys, prediction of the dynamics of political ratings, optimization of the production process, comprehensive diagnostics of product quality, etc. Utilisation of the hydraulic equipment makes it possible to design systems of automatic operation in the conditions, where minimal human participation and maximum speed of response are required. It is possible to state that such conditions exist in the regions with the permanent access of repair teams or technicians, who perform monitoring of the complex technical objects. Therefore, it is necessary to develop automated systems of operation and monitoring of various equipment components, which are intended for operation in the complicated technical conditions. Authors of this article have selected the hydraulic equipment as the object under investigation due to the fact that it is widely distributed equipment, as well as due to possibilities of this equipment to function or to be adapted for operation in practically any environmental conditions. At the same time, quantity of the state-of-the-art equipment, which is used, as well as complexity of this equipment increase very quickly, therefore process of making decision concerning utilisation of this equipment must be made very quickly. Authors analyse the sphere of automation, where utilisation of a human decision is required. The novelty of this article is connected with the assumption that further direction of operation of such equipment in the complicated technical conditions must be implemented in the sphere of guessing of the user’s actions. Authors review neural networks as the toolkit, and they believe that these networks can make decisions in the proactive mode, practically without participation of a user. This article includes description of the model, which can be used as the basis for the system, which is planned to design. In addition, methodological toolkit for assessment of efficiency of this model is proposed.