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2008-04-11
Adaptive Neuro-Fuzzy Inference System for the Computation of the Characteristic Impedance and the Effective Permittivity of the Micro-Coplanar Strip Line
By
Progress In Electromagnetics Research B, Vol. 6, 225-237, 2008
Abstract
A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The effective permittivity and the characteristic impedance results obtained by using ANFIS are in good agreement with the theoretical and experimental results reported elsewhere.
Citation
Nurcan Sarikaya, Kerim Guney, and Celal Yildiz, "Adaptive Neuro-Fuzzy Inference System for the Computation of the Characteristic Impedance and the Effective Permittivity of the Micro-Coplanar Strip Line," Progress In Electromagnetics Research B, Vol. 6, 225-237, 2008.
doi:10.2528/PIERB08031223
References

1. Yamashita, E., K. R. Li, and Y. Suzuki, "Characterization method and simple design formulas of MCS lines proposed for MMIC’s," IEEE Transaction on Microwave Theory and Techniques, Vol. 35, No. 12, 1355-1362, 1987.
doi:10.1109/TMTT.1987.1133860

2. Qian, Y. and E. Yamashita, "Additional approximate formulas and experimental data on micro-coplanar striplines," IEEE Transaction on Microwave Theory and Techniques, Vol. 38, No. 4, 443-445, 1990.
doi:10.1109/22.52590

3. Tan, K. W. and S. Uysal, "Analysis and design of conductor-backed coplanar waveguide lines using conformal mapping techniques and their application to end-coupled filters," IEICE Transactions on Electronics, Vol. 82, No. 7, 1999.

4. Sagiroglu, S. and C. Yildiz, "A multilayered perceptron neural network for a micro-coplanar strip line," Electromagnetics, Vol. 22, 553-563, 2002.
doi:10.1080/02726340290084111

5. Jang, J.-S. R., "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Transaction on System, Man and Cybernetics, Vol. 23, 665-668, 1993.
doi:10.1109/21.256541

6. Jang, J.-S. R., C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, 1997.

7. Guney, K. and N. Sarikaya, "Adaptive neuro-fuzzy inference system for the input resistance computation of rectangular microstrip antennas with thin and thick substrates," Journal of Electromagnetic Waves and Applications, Vol. 18, No. 1, 23-39, 2004.
doi:10.1163/156939304322749599

8. Guney, K. and N. Sarikaya, "Computation of resonant frequency for equilateral triangular microstrip antennas using the adaptive neuro-fuzzy inference system," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 14, 134-143, 2004.
doi:10.1002/mmce.10125

9. Guney, K. and N. Sarikaya, "Input resistance calculation for circular microstrip antennas using adaptive neuro-fuzzy inference system," International Journal of Infrared and Millimeter Waves, Vol. 25, 703-716, 2004.
doi:10.1023/B:IJIM.0000020756.48454.31

10. Turkmen, I. and K. Guney, "Tabu search tracker with adaptive neuro-fuzzy inference system for multiple target tracking," Progress In Electromagnetics Research, Vol. 65, 169-185, 2006.
doi:10.2528/PIER06090601

11. Daldaban, F., N. Ustkoyuncu, and K. Guney, "Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system," Energy Conversion and Management, Vol. 47, 485-493, 2006.
doi:10.1016/j.enconman.2005.05.020

12. Hilberg, W., "From approximations to exact relations for characteristic impedances," IEEE Transaction on Microwave Theory and Techniques, Vol. 17, 259-265, 1969.
doi:10.1109/TMTT.1969.1126946