A Computer Aided Design (CAD) approach based on Artificial Neural Networks (ANN's) is successfully introduced to determine the characteristic parameters of Circular-shaped Microshield and Conductor-Backed Coplanar Waveguide (CMCB-CPW). ANN's have been promising tools for many applications and recently ANN has been introduced to microwave modeling, simulation and optimization. The Multi Layered Perceptron (MLP) neural network used in this work were trained with Levenberg-Marquart (LM), Bayesian regularization (BR), Quasi-Newton (QN), Scaled Conjugate gradient (SCG), Conjugate gradient of Fletcher-Powell (CGF) and Conjugate Gradient backpropagation with Polak-Ribiere (CGP) learning algorithms. This has facilitated the usage of ANN models. The notable benefits are simplicity & accurate determination of the characteristic parameters of CMCBCPW's. The greatest advantage is lengthy formulas can be dispensed with.
2. Simons, R. N., "Coplanar Waveguide Circuits, Components and Systems," John Wiley & Sons, Inc., 2001.
3. Dib, N. I., W. P. Harokopus Jr., P. B. Katechi, C. C. Ling, and G. M. Rebeiz, "Study of a novel planar transmission line," IEEE MTT-S Digest, 623-626, 1991.
4. Lee, J.-W., I.-P. Hong, T.-H. Yoo, and H.-K. Park, "Quasi-static analysis of conductor backed coupled CPW," IEEE Electronics Letters, Vol. 34, No. 19, 1861-1862, 1998.
5. Gevorgian, S., L. J. Peter Linner, and E. L. Kollberg, "CAD models for shielded multilayered CPW," IEEE Trans. Microwave Theory Tech., Vol. 43, 772-779, 1995.
6. Du, Z. and C. Ruan, "Analytical analysis of circular-shaped microshield and conductor-backed coplanar wave guide," International Journal of Infrared and Millimeter Waves, Vol. 18, No. 1, 165-171, 1997.
7. Yildiz, C. and M. Turkmen, "Quasi-static models based on artificial neural networks for calculating the characteristic parameters of multilayer cylindrical coplanar waveguide and strip line," Progress In Electromagnetics Research B, Vol. 3, 1-22, 2008.
8. Kaya, S., M. Turkmen, K. Guney, and C. Yildiz, "Neural models for the elliptic- and circular-shaped microshield lines," Progress In Electromagnetics Research B, Vol. 6, 169-181, 2008.
9. Zhang, Q. J. and K. C. Gupta, "Neural Networks for RF and Microwave Design," Artech House, 2000.
10. Haykin, S., Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Comp., 1994.
11. Yildiz, C., K. Guney, M. Turkmen, and S. Kaya, "Neural models for coplanar strip line synthesis," Progress In Electromagnetics Research, Vol. 69, 127-144, 2007.
12. Fun, M.-H. and T. Martin Hagan, "Levenberg-marquardt training for modular networks," Proceedings of the 1997 International Joint Conference on Neural Networks, 468-473, 1996.
13. Levenberg, K., "A method for the solution of certain nonlinear problems in least squares," Quarterly of Applied Mathematics, Vol. 11, 431-441, 1963.
14. Mackay, D. J. C., "Bayesian interpolation," Neural Computation, Vol. 3, No. 4, 415-447, 1992.
15. Foresee, F. D. and M. T. Hagan, "Gauss-Newton approximation to Bayesian regularization," Proceedings of the 1997 International Joint Conference on Neural Networks, 1930-1935, 1997.
16. Gill, P. E., "Practical Optimization," Academic Press, 1981.
17. Fletcher, R. and C. M. Reeves, "Function minimization by conjugate gradients," Computer Journal, Vol. 7, 149-154, 1964.
18. Moller, M. F., "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, Vol. 6, 525-533, 1993.
19. Dennis, E. and R. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice Hall, 1983.