论文信息 - On the training patterns of a neural network for target localization in the spatial domain

On the training patterns of a neural network for target localization in the spatial domain

In this letter, we show that some spatial sampling requirements for the scattered electric field are necessary in order to obtain good performances when a neural-network approach is applied to the solution of target localization problems in the spatial domain. By means of some examples, concerning dielectric cylinders either in free space or buried in a lossy half space, we point out the dependence of the object localization on the spatial sampling rate of the scattered electric field and its relation to the field spatial bandwidth. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 28: 207–209, 2001.

Andrea Massa | Salvatore Caorsi | Mirco Raffetto | E. Bermani

[1] Mischa Schwartz,et al. Information transmission, modulation, and noise , 1959 .

[2] Michel Nakhla,et al. A neural network modeling approach to circuit optimization and statistical design , 1995 .

[3] Paolo Gamba,et al. Electromagnetic detection of dielectric cylinders by a neural network approach , 1999, IEEE Trans. Geosci. Remote. Sens..

[4] Francesco Carlo Morabito,et al. A neural network approach for the reduction of the dimensionality of slowly time-varying electromagnetic inverse problems , 1996 .

[5] R. J. Bastian,et al. An efficient neural network algorithm for reflector surface error compensation , 1996 .

[6] A. Raiche. A pattern recognition approach to geophysical inversion using neural nets , 1991 .

[7] Mahmood R. Azimi-Sadjadi,et al. Detection and classification of buried dielectric anomalies using neural networks-further results , 1994 .

[8] J. Bigeon,et al. Electromagnetic field parallel computation with a Hopfield neural network , 1995 .

[9] Satish S. Udpa,et al. Solution of inverse problems in electromagnetics using Hopfield neural networks , 1995 .