Method of Reference Models for Synthesis of Intelligent Systems for Identification of Nonlinear Dynamic Objects

The paper is devoted to resolving the contradiction between the accuracy of modeling nonlinear dynamics and the speed of model construction under conditions of limited computing resources. The aim of the work is to reduce the time for building models of nonlinear dynamic objects with continuous characteristics in the form of neural networks while ensuring a given modeling accuracy. This goal is achieved by developing a new method for synthesizing intelligent systems based on the superposition of pre-trained (support) models in the form of neural networks that reflect the basic properties of the object. The scientific novelty of the work lies in the development of a method for identifying nonlinear dynamic objects in the form of neural networks with time delays based on a set of pre-trained neural network models that reflect the basic properties of the subject area. In contrast to the traditional approach to pre-training, the developed method allows building models of lower complexity and with shorter training time while ensuring the required accuracy. To determine the initial parameters of the model, expressions based on the superposition of reference models in the form of neural networks are proposed. The practical utility of the work lies in the development of an algorithm for the method of reference models for training neural networks with time delays, which can significantly reduce the training time of neural networks without losing model accuracy