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Electromagnetic scattered field time series from finite difference time domain trained time delay neural network
Author(s) -
Sahoo Nihar K.,
Gouda Akhila,
Mishra Rashmirekha K.,
Parida Rajeev K.,
Panda Dhruba C.,
Mishra Rabindra K.
Publication year - 2020
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.22410
Subject(s) - finite difference time domain method , artificial neural network , time domain , computer science , time delay neural network , field (mathematics) , electromagnetic field , time series , pattern recognition (psychology) , algorithm , artificial intelligence , computer vision , mathematics , machine learning , optics , physics , quantum mechanics , pure mathematics
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.