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NEURAL NETWORK APPROACH TO SHAPE RECONSTRUCTION OF A CONDUCTING CYLINDER

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2007

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, the shape reconstruction of a conducting cylinder by making use of electromagnetic scattered fields is presented. The problem is treated with the Method of Moment (MoM) and Feed Forward Neural Networks (FF-NN) is applied. The shape of the cylinder is represented by a Fourier series while applying MoM, a matrix equation is obtained whose elements are expressed numerically by using discrete points. The scattered field data and Fourier coefficients of the cylinder are used for training the NN as inputs and outputs respectively. The NN results are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed. The effect of the noise on the scattering field is also investigated.
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  • 2
299-304
  • English

REFERENCES

References: 

[1] D. Colton, R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, Springer-Verlag, Berlin Heidelberg New York 1999.
[2] C. Christodoulou, M. Georgiopoulos, Applications of Neural Networks in Electromagnetics, Artech House, 2001.
[11] U. Asık, T. Günel, I. Erer, “Wavelet Based Radial Function Neural Network Approach to the Inverse Scattering of Conducting Cylinders”, Microwave Opt Tech. Let., Vol. 41, No 6 (2004), 506–511.
[3] I. Elsha.ey, L. Udpa, S.S. Udpa, “Solution of inverse problems in Electromagnetics using hop.eld neural Networks”, IEEE Tran. Magn 31 (1995), 852–861.
[12] R. F. Harrington, Field Computation by Moment Method, Wiley IEEE Press, 21 April 1993, New York, USA.
[4] S. Ratnajeevan and H. Hoole, “Artificial neural networks in the solution of inverse electromagnetic field problems”, IEEE Trans Magn, 29 (1993), 1931–1934.
[13] P. R.Gill, W. Murray, M. H. Wright, The Levenberg-Marquardt Method, Practical Optimization, London: Academic Press, [5] S. Caorsi and P. Gamba, “Electromagnetic detection of dielectric cylinders by a neural network approach”, IEEE Trans Geoscience Remote Sensing,37 (1999), 820–827.
[6] R. Mydur and K.A. Michalski, “A neural-network approach to the electromagnetic imaging of elliptic conducting cylinders”, Microwave Opt Tech. Let, 28 (2001), 303–306.
[7] E. Bermani, S. Coarsi, and M. Raffetto, “A threshold electromagnetic classification approach for cylinders embedded in a lossy medium by using a neural network technique”, Microwave Opt Technol Lett, 24(2000), 13–16.
[8] I. T. Rekanos, “Neural-network-based inverse-scattering technique for online microwave medical imaging”, IEEE Trans Mag. 38 (2002), 1061–1064.
[9] E. Bermani, S. Coarsi, and M. Raffetto, “Microwave detection and dielectric characterization of cylindrical objects from amplitude-only data by means of neural Networks”, IEEE Trans Antennas Prop., 50(2002), 1309–1314.
[10] I. T. Rekanos, “On-line inverse scattering of conducting cylinders using radial basis-function neural Networks”, Microwave Opt Tech. Lett, 28 (2001), 378–380.

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