In this paper, we proposed an efficient knowledge-based Support Vector Regression Machine (SVRM) method and applied it to the synthesis of the transmission lines for the microwave integrated circuits, with the highest possible accuracy using the fewest accurate data. The technique has integrated advanced concepts of SVM and knowledge-based modeling into a powerful and systematic framework. Thus, synthesis model as fast as the coarse models and at the same time as accurate as the fine models is obtained for the RF/Microwave planar transmission lines. The proposed knowledge-based support vector method is demonstrated by a typical worked example of microstrip line. Success of the method and performance of the resulted synthesis model is presented and compared with ANN results.
1. Zhang, Q. J. and K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, Norwood, MA, 2000.
2. Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
3. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM based estimatiors for inverse scattering problems," Progress In Electromagnetics Research, PIER 53, 167-188, 2005.
4. Gunes, F., N. T. Tokan, and F. Gurgen, "Signal-noise support vector model of a microwave transistor," Int. J. RF and Microwave CAE, Vol. 17, 404-415, 2007. doi:10.1002/mmce.20239
5. Gunes, F., N. T. Tokan, and F. Gurgen, "Support vector design of the microstrip lines," Int. J. RF and Microwave CAE, Vol. 18, 326-336, 2008. doi:10.1002/mmce.20290
6. Tokan, N. T. and F. Gunes, "Support vector characterisation of resonance frequencies of microstrip antennas based on measurements," Progress In Electromagnetics Research B, Vol. 5, 49-51, 2008. doi:10.2528/PIERB08013006
7. Wang, F. and Q. J. Zhang, "Knowledge-based neural models for microwave design," IEEE Trans. Microwave Theory Tech., Vol. 45, 2333-2343, Dec. 1997. doi:10.1109/22.643839
9. Jargon, J. A., K. C. Gupta, and D. C. DeGroot, "Applications of artificial neural networks to RF and microwave measurements," Int. J. RF Microwave Computer-aided Eng., Vol. 12, 3-24, 2002. doi:10.1002/mmce.10014
10. Bandler, J. W., M. A. Ismail, J. E. Rayas-Sanchez, and Q. J. Zhang, "Neuromodeling of microwave circuits exploiting space-mapping technology," IEEE Trans. Microwave Theory Tech., Vol. 47, 2417-2427, Dec. 1999. doi:10.1109/22.808989
11. Bakr, M. H., J. W. Bandler, M. A. Ismail, J. E. Rayas-Sanchez, and Q. J. Zhang, "Neural space-mapping optimization for EM-based design," IEEE Trans. Microwave Theory Tech., Vol. 48, 2307-2315, Dec. 2000. doi:10.1109/22.898979
12. Devabhaktuni, V. K., M. C. E. Yagoub, and Q. J. Zhang, "A robust algorithm for automatic development of neural network models for microwave applications," IEEE Trans. Microwave Theory Tech., Vol. 49, 2282-2291, Dec. 2001. doi:10.1109/22.971611
13. Bandler, J. W., J. E. Rayas-Sanchez, and Q. J. Zhang, "Yielddriven electromagnetic optimization via space mapping-based neuromodels," Int. J. RF Microwave Computer-aided Eng., Vol. 12, 79-89, 2002. doi:10.1002/mmce.10015
14. Devabhaktuni, V. K., B. Chattaraj, M. C. E. Yagoub, and Q. J. Zhang, "Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping," IEEE Trans. Microwave Theory Tech., Vol. 51, No. 7, 1822-1833, Jul. 2003. doi:10.1109/TMTT.2003.814318
15. Edwards, T. C., "Foundations for microstrip circuit design,", Wiley-Interscience, New York, 1981.