Fast Prediction Model of Coupling Coefficient Between Pantograph Arcing and GSM-R Antenna

Abstract

In this article, a fast prediction model based on machine learning is established to predict the coupling coefficient between pantograph arcing, and GSM-R antenna. Compared with the method of solving coupling coefficient based on electromagnetic simulation, the prediction model established in this article can not only ensure the accuracy of coupling coefficient, but also avoid a lot of repeated simulation, and reduce the simulation time. First, the data set is constructed by Latin hypercube sampling. Then, by analyzing the distribution characteristics of the data set, the Radial Based Function (RBF), and Generalized Regression Neural Network (GRNN) are selected for modeling. Furthermore, in order to improve the prediction precision, Modular Neural Network (MNN) is introduced, and the collaborative integration strategy of segmented prediction is proposed, which realizes the complementary advantages of RBF, and GRNN, and completes the construction of prediction model. Finally, the precision, and validity of the prediction model is verified by the analysis of the prediction results, relative prediction deviation, and other related parameters.