Structural transformations of a neural network controller with a recurrent network type

The complexity of the objects of regulation, as well as the increase in the requirements for the productivity of the applied regulators, leads to the complexity of the applied neural network regulators. One of the complications is the appearance of feedback loops in the regulator. That is, the transition from direct distribution networks to re-current ones. One of the problems when using them is setting up weight coefficients using methods based on gradient calculation (for example, the error propagation method, the Levenberg-Marquardt method, etc.). It manifests itself in a suddenly "dis-appearing" or "exploding" gradient, which means that the learning process of the net-work stops. The purpose of this article is to develop proposals for solving some problems of con-figuring the weight coefficients of a recurrent neural network. As methods for achieving this goal, structural transformations of the architecture of a recurrent neural network are used to bring it to the form of a direct distribution net-work. At the same time, there is a slight increase in the complexity of its architecture. For networks of direct distribution methods based on the computation of the inverse gradient can be used without modification. In the future, it is planned to increase the performance of regulating the system with the help of a converted neuro-regulator, namely, to reduce the over-regulation of the system and, after some complications of the structure, use it to regulate a nonlinear object.