Neural network identification of a nonlinear model of a high-pressure sodium lamp

Abstract

The article is devoted to the identification of a high-pressure sodium lamp nonlinear model parameters based on neural network technologies. Identification was carried out using a dynamic neural network. The model had 17 parameters based on second order differential equations. As a result, out of 17 parameters, four were selected that accurately reflect the real picture of the model.