Buradasınız

ONE DIMENSIONAL PROFILE RECONSTRUCTION OF DIELECTRIC CYLINDER BY USING NEURAL NETWORK

Journal Name:

Publication Year:

Author NameUniversity of Author
Abstract (2. Language): 
In this paper, Neural Network (NN) is used for profile reconstruction of dielectric constant of the cylinder. The region is assumed to be filled with dielectric medium of which dielectric constant changes along the radial direction. Radial variation of dielectric constant is represented by Fourier series. Scattered fields, which are obtained by numerically Method of Moments (MoM) technique, and Fourier coefficients are used for training the NN as inputs and outputs respectively. In numerical examples, Scattered fields are applied trained NN, outputs of NN are compared the exact profiles and good reconstructions are observed.
797-800

REFERENCES

References: 

[1] Colton, D., Kress, R., “Inverse Acoustic and
Electromagnetic Scattering Theory”, 2nd. ed.
Springer-Verlag, Berlin Heidelberg New York
1999.
[2] Christodoulou C., M. Georgiopoulos,
“Applications of Neural Networks in
Electromagnetics”, Artech House, 2001.
[3] Elsha I., Udpa L., Udpa S.S., “Solution of
inverse problems in Electromagnetics using
hopfield neural networks”, IEEE Tran. Mag. 31
pp. 852–861,1995.
[4] Ratnajeevan S., Hoole H., “Artificial neural
networks in the solution of inverse
electromagnetic field problems”, IEEE Trans.
Mag., 29, pp. 1931–1934, 1993.
[5] Caorsi S., Gamba P., “Electromagnetic
detection of dielectric cylinders by a neural
network approach”, IEEE Trans. Geoscience
Remote Sensing, 37 pp. 820–827,1999.
[6] Mydur R., Michalski K.A., “A neuralnetwork
approach to the electromagnetic
imaging of elliptic conducting cylinders”,
Microwave Opt. Tech. Lett. 28, pp. 303–
306,2001.
[7] Bermani E., Coarsi S., Raffetto M., “A
threshold electromagnetic classification approach
for cylinders embedded in a lossy medium by
using a neural network technique”, Microwave
Opt. Tech.Let., 24(2000), pp. 13–16.
[8] Rekanos I.T., “Neural-network-based
inverse-scattering technique for online
microwave medical imaging”, IEEE Trans.
Mag., 38, pp. 1061–1064,2002.
[9] Bermani E., Coarsi S., Raffetto M.,
“Microwave detection and dielectric
characterization of cylindrical objects from
amplitude-only data by means of neural
networks”, IEEE Trans. Antennas. Prop., 50, pp.
1309–1314,2002.
[10] Rekanos I.T., “On-line inverse scattering of
conducting cylinders using radial basis-function
neural networks”, Microwave Opt. Tech. Lett.,
28, 378–380,2001.
[11] Asık U., Günel T., Erer I., “Wavelet Based
Radial Function Neural Network Approach to the
Inverse Scattering of Conducting Cylinders”,
Microwave Opt. Tech. Let., Vol: 41, No. 6, pp.
506–511,2004.

Thank you for copying data from http://www.arastirmax.com