Support vector design of the microstrip lines

International Journal of RF and Microwave Computer-Aided Engineering

Volumn 18, Issue 4, 2008, Pages 326-336

  Güneş, Filiz ^a   Tokan, Nurhan Türker ^a   Gürgen, Fikret ^b  

^a YILDIZ TECHNICAL UNIVERSITY   (Turkey)

^b BOGAZICI UNIVERSITY   (Turkey)

Author keywords

Adaptive step size; Artificial neural networks; Microstrip lines; Reverse modeling; Support vector regression

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; COMPUTATIONAL EFFICIENCY; FOOD PROCESSING; MICROSTRIP LINES; NEURAL NETWORKS; PROCESS DESIGN; PROCESS ENGINEERING; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

ADAPTIVE STEP SIZE; ANALYSIS AND SYNTHESIS; ARTIFICIAL NEURAL NETWORKS (ANN); BLACK BOXES; DESIGN PROCESSES; FASTER CONVERGENCE; MICROSTRIP (MSL); MICROSTRIPLINE (MSL); REGRESSION APPLICATIONS; SPARSE SOLUTIONS; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTORS; VECTOR MACHINES; WORKED EXAMPLES;

DIELECTRIC MATERIALS;

EID: 47149095771     PISSN: 10964290     EISSN: 1099047X     Source Type: Journal    
DOI: 10.1002/mmce.20290     Document Type: Article

References (16)

1. V.N. Vapnik, Statistical learning theory, Wiley, New York, 1998.
2. N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines (and other kernel-based learning methods), Cambridge University Press, Cambridge, 2000.
3. A. Ganapathiraju, J.E. Hamaker, and J. Picone, Applications of support vector machines to speech recognition, IEEE Trans Signal Process 52 (2004), 2348-2356.
4. J.L. Rojo-Álvarez, G. Camps-Valls, M. Martí́nez- Ramón, E. Soria-Olivas, A. Navia-Vázquez, and A.R. Figueiras-Vidal, Support vector machines framework for linear signal processing, Signal Process 85 (2005), 2316-2326.
5. J.L. Rojo-Álvarez, M. Martínez-Ramón, A.R. Figueiras-Vidal, A. García-Armada, and A. Artés-Rodríguez, A robust support vector algorithm for nonparametric spectral analysis, IEEE Signal Process Lett 10 (2003), 320-323.
6. C. Christodoulou, M. Martinez-Ramon, and C. Balanis, Support vector machines for antenna array processing and electromagnetics, Morgan & Claypool Publishers, Co, USA 2006.
7. M. Pastorino and A. Randazzo, A smart antenna system for direction of arrival estimation based on a support vector regression, IEEE Trans Antennas Propag 53 (2005), 2161-2168.
8. Q. Zhao and J. Principe, Automatic target recognition with support vector machines, Neural Information Processing Systems Workshop on Large Margin Classifiers, Denver USA, December 1998.
9. L. Xia, J. Meng, R. Xu, B. Yan, and Y. Guo, Modeling of 3-D vertical interconnect using support vector machine regression, Microwave Wireless Compon Lett IEEE 16 (2006), 639-641.
10. G. Angiulli, M. Cacciola, and M. Versaci, Microwave devices and antennas modeling by support vector regression machines, IEEE Trans Magn 43 (2007), 1589-1592.
11. J. Mercer, Functions of positive and negative type and their connection with the theory of integral equations, Philos Trans R Soc London Ser A 209 (1909), 415-446.
12. F. Güneş, N. Türker, and F. Gürgen, Signal-noise support vector model of a microwave transistor, Int J RF Microwave CAE 17 (2007), 404-415.
13. R.E. Collins, Foundations for microwave engineering, McGraw-Hill, New York, 1992.
14. I. Bahl and P. Bhartia, Microwave solid-state design, Wiley-Interscience, New York, 1988.
15. T.C. Edwards, Foundations for microstrip circuit design, Wiley-Interscience, New York, 1981.
16. F. Güneş and N. Türker, Artificial neural networks in their simplest forms for analysis and synthesis of RF/ microwave planar transmission Lines, Int J RF Microwave CAE 15 (2005), 587-600.