Neural Network Based Semi-Empirical Models for Dynamical Systems Represented by Differential-Algebraic Equations of Index 2

Abstract

A simulation problem is discussed for nonlinear controlled dynamical systems represented by differential-algebraic equations of index 2. The problem is proposed to be solved in the framework of a neural network based semi-empirical approach combining theoretical knowledge for the object with training tools of artificial neural network field. Special form neural network based semi-empirical models implementing implicit Runge-Kutta method of numerical integration are proposed. The training of the semi-empirical model allows to elaborate the models of aerodynamic coefficients implemented as a part of it. The results of simulation using this elaboration procedure of lift coefficient for reentry hypersonic vehicle are presented.