Vol. 1
Latest Volume
All Volumes
PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2008-02-06
Neural Frequency Sweeper for Accelerating s -Parameters Calculation of Planar Microwave Structures
By
Progress In Electromagnetics Research M, Vol. 1, 31-43, 2008
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.
Citation
Ezzeldin A. Soliman Mourad Ibrahim , "Neural Frequency Sweeper for Accelerating s -Parameters Calculation of Planar Microwave Structures," Progress In Electromagnetics Research M, Vol. 1, 31-43, 2008.
doi:10.2528/PIERM08010702
http://www.jpier.org/PIERM/pier.php?paper=08010702
References

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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.

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