Electromagnetic scattered field time series from finite difference time domain trained time delay neural network

Abstract

This paper uses time delay neural network (TDNN) for predicting electromagnetic (EM) fields scattered from dielectric objects (cylinder, cylinder-hemisphere, and cylinder-cone) using: (a) FDTD generated initial field data for similar conducting objects and (b) Statistical information for the nature of fields. Statistical data indicated that the scattered field nature is close to deterministic. The TDNN structure determination uses statistical data for fixing the number of delays and tabular technique to obtain the number of hidden neurons. The TDNN training uses the Levenberg-Marquardt (LM) algorithm. The model outputs follow standard FDTD results closely.