NEUROCOMPUTER CYBER-PHYSICAL MONITORING SYSTEM FOR ELECTRICAL INSULATION MATERIALS OF CABLE NETWORKS
The article is devoted to the study of the structure of electrical insulating materials (EM) of power cable lines (PCL). Within the framework of the conducted research, a neural network (NN) model describing the architecture of a cyberphysical system (CFS) for predicting the resource of EM cable electrical networks was built. The NS emulator uses the NN output instead of the object output. This allows you to make a forecast of the dynamics of the object for many steps ahead. An algorithmic solution for automatic parameter selection and NS training with subsequent forecasting has been synthesized, which makes it possible to increase the reliability of the CFS by reducing the time to create the optimal configuration of the NN. The representation of a dynamic object based on NS is fundamentally different from the description of a dynamic object using differential equations. The obtained NS model can be effectively used for the analysis of thermal fluctuation processes occurring in the control object - PCL, and the prediction of the behavior of the object.