volume | PIER Journals

Abstract

This paper presents a new frequency-sweep approach for the efficient calculation of S-parameters of planar microwave structures. The approach is based on approximating the frequency dependence of the real and imaginary parts of the S-parameters using neural networks. Due to its superior performance, radial basis functions neural network (RBF-NN) is adopted. A limited number of frequency samples are used to train the RBF-NN. Then, the trained RBF-NN is capable of providing a smooth frequency response with very high accuracy in a fraction of a second. The proposed method is applied to a number of planar microwave structures such as: Patch antenna with an inset feed, band-rejection filter, and branch-line coupler. According to the presented results, a speed factor of at least 10 is measured, and a maximum percentage error of 3.29% is recorded.

1. Maren, A., C. Harston, and R. Pap, Handbook of Neural Computing Applications, Academic Press, 1990.

2. Zhang, Q. J. and K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, 2000.

3. Watson, P. and K. C. Gupta, "EM-ANN models for microstrip vias and interconnects in dataset circuits," IEEE Trans. Microwave Theory Tech., Vol. 44, 2495-2503, Dec. 1996.
doi:10.1109/22.554584 Google Scholar

4. Soliman, E. A., M. H. Bakr, and N. K. Nikolova, "Modeling of microstrip lines using neural networks — Applications to the design and analysis of distributed microstrip circuits," Int. J. RF and Microwave Computer-Aided Eng., Vol. 14, 166-173, March 2004.
doi:10.1002/mmce.10127 Google Scholar

5. Guney, K., C. Yildiz, S. Kays, and M. Turkmen, "Artificial neural networks for calculating the characteristic impedance of airsuspended trapezoidal and rectangular-shaped microshield lines," Journal of Electromagnetic Waves and Applications, Vol. 20, 1161-1174, 2006.
doi:10.1163/156939306777442917 Google Scholar

6. Zaabab, A. H., Q.-J. Zhang, and M. Nakhla, "A neural network approach to circuit optimization and statistical design," IEEE Trans. Microwave Theory Tech., Vol. 43, 1349-1358, June 1995.
doi:10.1109/22.390193 Google Scholar

7. Mishra, R. K. and A. Patnaik, "Neural network-based CAD model for the design of square-patch antennas," IEEE Trans. Antennas Propagat., Vol. 46, 1890-1891, Dec. 1998.
doi:10.1109/8.743842 Google Scholar

8. Mohamed, M. D. A., E. A. Soliman, and M. A. El-Gamal, "Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks," Journal of Electromagnetic Waves and Applications, Vol. 20, 1101-1114, 2006.
doi:10.1163/156939306776930240 Google Scholar

9. El-Zooghby, A. H., C. G. Christodoulou, and M. Georgiopoulos, "Performance of radial basis function networks for direction of arrival estimation with antenna array ," IEEE Trans. Antennas Propagat., Vol. 45, 1611-1617, Nov. 1997.
doi:10.1109/8.650072 Google Scholar

10. Zainud-Deen, S. H., H. A. Malhat, K. H. Awadalla, and E. S. El-Hadad, "Direction of arrival and state of polarization estimation using radial basis function neural network (RBFNN)," Progress In Electromagnetics Research B, Vol. 2, 137-150, 2008.
doi:10.2528/PIERB07111801 Google Scholar

11. Zhao, Q. and Z. Bao, "Radar target recognition using a radial basis function," Neural Networks, Vol. 9, 709-720, April 1996.
doi:10.1016/0893-6080(96)00088-3 Google Scholar

12. Washington , G., "Aperture antenna shape prediction by feed forward neural networks," IEEE Trans. Antennas Propagat., Vol. 45, 683-688, April 1997.
doi:10.1109/8.564094 Google Scholar

13. Rekanos, I. T., "Inverse scattering of dielectric cylinders by using radial basis function neural networks ," Radio Science, Vol. 36, 841-849, Sept. 2001.
doi:10.1029/2000RS002545 Google Scholar

14. Ayestaran, R. G. and F. Las-Heras, "Near field to far field transformation using neural networks and source reconstruction," Journal of Electromagnetic Waves and Applications, Vol. 20, 2201-2213, 2006.
doi:10.1163/156939306779322594 Google Scholar

15. Ayestaran, R. G., J. Laviada, and F. Las-Heras, "Synthesis of passive-dipole arrays with a genetic-neural hybrid method," Journal of Electromagnetic Waves and Applications, Vol. 20, 2123-2135, 2006.
doi:10.1163/156939306779322549 Google Scholar

16. Ayestaran, R. G., F. Las-Heras, and J. A. Martinez, "Non uniform-antenna array synthesis using neural networks," Journal of Electromagnetic Waves and Appls, Vol. 21, 1001-1011, 2007. Google Scholar

17. Soliman, E. A., M. H. Bakr, and N. K. Nikolova, "Neural Networks — Method of Moments (NN-MoM) for the efficient filling of the coupling matrix," IEEE Transactions on Antennas and Propagation, Vol. 52, 1521-1529, June 2004.
doi:10.1109/TAP.2004.829846 Google Scholar

18. Soliman, E. A., M. A. El-Gamal, and A. K. Abdelmageed, "Neural network model for the efficient calculation of Green's functions in layered media ," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 13, 128-135, March 2003.
doi:10.1002/mmce.10066 Google Scholar

19. Ling, F., D. Jiao, and J.-M. Jin, "Efficient electromagnetic modeling of microstrip structures in multilayer media," IEEE Trans. Microwave Theory Tech., Vol. 47, 1810-1818, Sept. 1999.
doi:10.1109/22.788516 Google Scholar

20. Newman, E. H., "Generation of wide-band data from the method of moments by interpolating the impedance matrix," IEEE Trans. Antennas Propagat., Vol. 36, 1820-1824, Dec. 1988.
doi:10.1109/8.14404 Google Scholar

21. Virga, K. and Y. Rahmat-Samii, "Efficient wide-band evaluation of mobile communications antennas using [Z ] or [ Y ] matrix interpolation with the method of moments," IEEE Trans. Antennas Propagat., Vol. 47, 65-76, Jan. 1999.
doi:10.1109/8.752990 Google Scholar

22. Yeo, J. and R. Mittra, "An algorithm for interpolating the frequency variations of method-of-moments matrices arising in the analysis of planar microstrip structures," IEEE Trans. Microwave Theory Tech., Vol. 51, 1018-1025, March 2003.
doi:10.1109/TMTT.2003.808703 Google Scholar

23. Soliman, E. A., "Rapid frequency sweep technique for MoM planar solvers," IEE Proceedings Microwaves, Antennas & Propagation, Vol. 151, 277-282, Aug. 2004.
doi:10.1049/ip-map:20040646 Google Scholar

24. MATLAB, version 7.0, The MathWorks Inc., 2004.

25. Jokinen, P. A., "Neural networks with dynamic capacity allocation and quadratic function neurons," Proc. of NEURO-Nimes 90, Nimes, France, 1990. Google Scholar

26. ADS/Momentum, version 4.7, Agilent Technologies, 2002.

Professor Ezzeldin A. Soliman

Dr. Mourad Ibrahim