NFDTD concept

Author

Mishra, Rabindra K. ; Hall, Peter S.

Author_Institution

Sambalpur Univ., Orissa, India

Volume

16

Issue

2

fYear

2005

fDate

3/1/2005 12:00:00 AM

Firstpage

484

Lastpage

490

Abstract

This paper combines artificial neural network (ANN) technique with the finite difference time domain (FDTD) technique. A detailed illustration of the concept, in this paper, uses a 3-8-1 feedforward artificial neural network (FF-ANN) for approximating the Z-component of the electric field in a rectangular waveguide in TM mode. The FDTD equation (i.e., the two-dimensional (2-D) wave equation in discrete form) is embedded into the cost function of the ANN. Results of implementing this technique in a one-dimensional (1-D) transmission line resonator are also provided with 4-10-1 FF-ANN. The result of the leap-frog algorithm implementation, for this 1-D problem using a (3-6-1) × (3-6-1) hybrid FF-ANN, is also provided. The neural-finite difference time domain (NFDTD) results are compared with those of the traditional FDTD.

Keywords

artificial intelligence; computational electromagnetics; feedforward neural nets; finite difference time-domain analysis; rectangular waveguides; computational electromagnetic; feedforward artificial neural network; leap frog algorithm; neural finite difference time domain; rectangular waveguide; Artificial neural networks; Difference equations; Educational institutions; Electromagnetic waveguides; Finite difference methods; Integral equations; Magnetic separation; Partial differential equations; Sparse matrices; Time domain analysis; Finite difference time domain (FDTD); neural network; neural-finite difference time domain (NFDTD); resonator; waveguide;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2004.841799

Filename

1402508