Improving the Accuracy of the Neural Network Models Interpretation of Nonlinear Dynamic Objects

Abstract

The paper is devoted to the problem of neural network interpretation in the tasks of modeling nonlinear dynamic objects. The purpose of the work is to improve the accuracy of the neural network models interpretation of nonlinear dynamic objects and to determine the scope of their effective application. This goal is achieved by applying analytical models in the form of integral-power series based on multidimensional weight functions. The scientific novelty of the work lies in the use of nonlinear dynamic models in the form of integral-power series based on multidimensional weight functions instead of linear surrogate models. It allows to improve modeling accuracy. The practical usefulness of the work is determination of the effective application area of analytical interpretive models. The practical significance of the obtained results lies in the application of the proposed models for the interpretation of neural network models of nonlinear dynamic objects, which allows to increase the accuracy of interpretation models compared to linear surrogate models.