Premium
FDTD signal extrapolation using a finite impulse response neural network model
Author(s) -
Wu Chen,
Navarro Enrique A.,
Navasquillo Joaquin,
Litva John
Publication year - 1999
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/(sici)1098-2760(19990605)21:5<325::aid-mop6>3.0.co;2-c
Subject(s) - finite difference time domain method , finite impulse response , artificial neural network , extrapolation , time domain , nonlinear system , backpropagation , impulse (physics) , impulse response , computer science , microwave , infinite impulse response , electronic engineering , filter (signal processing) , algorithm , digital filter , engineering , mathematics , physics , telecommunications , artificial intelligence , mathematical analysis , optics , quantum mechanics , computer vision
The finite impulse response (FIR) neural network technique is applied as a nonlinear predictor to extrapolate a time‐series signal from FDTD simulations. The FIR neural networks are trained by the temporal backpropagation learning algorithm. The model is applied to predict time‐domain evolution in the FDTD simulation of a waveguide filter. ©1999 John Wiley & Sons, Inc. Microwave Opt Technol Lett 21: 325–330, 1999.