Organization of intelligent control systems on the basis of neuroregulation
The article considers a number of proposals aimed at improving the efficiency of the design, training and operation of neural network models within the framework of control systems for complex objects. This includes: organization of processing of networks of large dimenstionality; development of neuroevolutionary procedures; providing opportunities for the formation of neural network models in a single cycle of designing automated systems. The hierarchical construction of network structures is considered as an apparatus that provids work with network models of high dimensionality. At the same time, unlike the traditional approach associated with cutting the graph of an already synthesized model, the article proposes procedures for synthesizing a generalized model from structural elements. The formal descriptions of structural synthesis introduced in the article can serve as a basis for organizing software and hardware interfaces of the forming models. The formation of such neural network structures within control systems is associated with the proposed and described concept of neural network regulators as the basic elements of building intelligent automated systems. The article also examines and describes as a neuroelement a model of a dynamic neuron with a state memory, allowing to implement the mechanism of self-evolutionary development of the network directly in the course of its operation.