Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T16:47:56.719Z Has data issue: false hasContentIssue false
  • English
  • Français

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

Published online by Cambridge University Press:  28 November 2016

Chandan Roy ,
Taimoor Khan  and
Binod Kumar Kanaujia
Chandan Roy
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Cachar, Assam, India
Taimoor Khan*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Cachar, Assam, India
Binod Kumar Kanaujia
Affiliation:
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
*
Corresponding author: T. Khan Email: ktaimoor@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Artificial neural networks (ANNs) have acquired enormous importance in computing of the performance parameters of microstrip antennas due to their generalized and adaptive features. However, recently the concept of support vector machines (SVMs) has become very much popular in performance parameters computation due to several attractive features over ANNs. Specifically, SVMs outreach ANNs noticeably in terms of execution time. Likewise, ANNs are having multiple local minima problem, whereas a global and unique solution is provided by SVMs. In this paper, several performance parameters like: resonant frequency, gain, directivity, and radiation efficiency of slotted microstrip antennas with modified ground plane are computed with the help of SVM formulation. Comparisons of different parameters of simulated and computed values are illustrated. The achieved radiation patterns at particular resonant frequency in different planes are included as well. A prototype of the optimized antenna is also fabricated and characterized. A good agreement is attained among the computed, simulated, and measured results.

Keywords

SVMMicrostrip antennaSlotted antennaModified ground planeRectangular patchPerformance parametersDifferent slots
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 9 , Issue 5 , June 2017 , pp. 1169 - 1177
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Bahl, I.J.; Bhartia, P.: Microstrip Antennas, Artech House, Dedham, MA, 1980.Google Scholar
[2] Garg, R.; Bhartia, P.; Bahl, I.; Ittipiboon, A.: Microstrip Antenna Design Handbook, Artech House, Boston, 2001.Google Scholar
[3] Khan, T.; De, A.; Uddin, M.: Prediction of slot-size and inserted air-gap for improving the performance of rectangular microstrip antennas using artificial neural networks. IEEE Antennas Wireless Propag. Lett., 12 (1) (2013), 1367–1371.Google Scholar
[4] Khan, T.; De, A.: Estimation of different performance parameters of slotted microstrip antennas with air-gap using neural networks, in ISRN Electronics, Hindawi Publishing Co. USA, vol. 2014, 2014, Article ID 296105, 6 pp.Google Scholar
[5] Khan, T.; De, A.: Estimation of radiation characteristics of different slotted microstrip antennas using a knowledge-based neural networks model. Int. J. RF Microw. Comput. Aided Eng., 24 (6) (2014), 673–680.Google Scholar
[6] Khan, T.; De, A.: Modeling of microstrip antennas using neural network techniques: a review. Int. J. RF Microw. Comput. Aided Eng., 25 (2015), 747–757.Google Scholar
[7] Khan, T.; De, A.: Prediction of slot-shape, slot-size and inserted air-gap of a microstrip antenna using knowledge-based neural network. Progr. Electromagn. Res. C, 63 (2016), 23–32.Google Scholar
[8] Neog, D.K.; Pattnaik, S.S.; Panda, D.C.; Devi, S.; Khuntia, B.; Dutta, M.: Design of a wideband microstrip antenna and the use of artificial neuralnetworks in parameter calculation. IEEE Antennas Propag. Mag., 47 (3) (2005), 60–65.Google Scholar
[9] Lebbar, S.; Guennoun, Z.; Drissi, M.; Riouch, F.: A compact and broadband microstrip antenna design using a geometrical-methodology based artificial neural network. IEEE Antennas Propag. Mag., 48 (2) (2006), 146–154. http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=32695.Google Scholar
[10] Guney, K.; Sarikaya, N.: a hybrid method based on combining artificial neural network and fuzzy inference system for simultaneous computation of resonant frequencies of rectangular, circular, and triangular microstrip antennas. IEEE Trans. Antennas Propag., 55 (3) (2007), 659–668. http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=32695 Google Scholar
[11] Bose, T.; Gupta, N.: Design of an aperture-coupled microstrip antenna using a hybrid neural network. IET Microw., Antennas Propag., 6 (4) (2012), 470–474.Google Scholar
[12] Wang, Z.; Fang, S.; Wang, Q.; Liu, H.: An ANN-based synthesis model for the single-feed circularly-polarized square microstrip antenna with truncated corners. IEEE Trans. Antennas Propag., 60 (12) (2012), 5989–5992.Google Scholar
[13] Vapnik, V.N.: The Nature of Statistical Learning Theory, Springer–Verlag, New York, 1995.Google Scholar
[14] Hearst, M.A.; Dumais, S.T.; Osman, E.; Platt, J.: Support vector machines. IEEE Intell. Syst. Appl., 13 (4) (1998), 18–28.Google Scholar
[15] Mattera, D.; Palmieri, F.; Haykin, S.: An explicit algorithm for training support vector machines. IEEE Signal Process. Lett., 6 (9) (1999), 243–245.Google Scholar
[16] Feijoo, J.; Rojo-Alvarez, J.L.; Sueiro, J.C.; Conde-Pardo, P.; Mata-Vigil-Escalera, J.L.: Modeling link events in high reliability networks with support vector machines. IEEE Trans. Reliab., 59 (1) (2010), 191–202.Google Scholar
[17] Wang, W.; Xu, D.; Qiu, L.: Support vector machine with chaotic genetic algorithms for annual runoff forecasting, in Proc. IEEE 6th Int. Conf. Natural Computation, vol. 2, Yantai, Shandong, August 2010, 671–675.Google Scholar
[18] Kao, J.W.H.; Berber, S.M.; Kecman, V.: Blind multiuser detector for chaos based CDMA using support vector machine. IEEE Trans. Neural Networks, 21 (8) (2010), 1221–1231.Google Scholar
[19] Gunes, F.; Tokan, N.T.; Gurgen, F.: A knowledge-based support vector synthesis of the transmission lines for use in microwave integrated circuits. Expert Sys. Appl., 37 (4) (2010), 3302–3309.CrossRefGoogle Scholar
[20] Wang, F.F.; Zhang, Y.R.: The support vector machine for dielectric target detection through a wall. Progr. Electromagn. Res. Lett., 23 (2011), 119–128.Google Scholar
[21] Li, X.; Guo, H.; Sun, Z.; Shen, G.: Urban impervious surfaces estimation from RADARSAT-2 polarimetric data using SVM method, in Proc. Progress in Electromagnetics Research Symp., Suzhou, China, September 2011, 807–812.Google Scholar
[22] Hsieh, T.J.; Yeh, W.C.: Knowledge discovery employing grid scheme least squares support vector machines based on orthogonal design bee colony algorithm. IEEE Trans. Syst. Man Cybern. B, Cybern., 41 (5) (2011), 1198–1211.Google Scholar
[23] Charrada, A.; Samet, A.: Estimation of highly selective channels for OFDM system by complex least squares support vector machines. AEU-Int. J. Electron. Commun., 66 (8) (2012), 687–692. http://www.sciencedirect.com/science/journal/14348411/66/8.Google Scholar
[24] Peng, H.; Yang, T.; Yang, Z.Q.: Calibration of a six-port position sensor via support vector regression. Progr. Electromagn. Res. C, 26 (2012), 71–81.Google Scholar
[25] Mo, F.; Lu, Y.; Zhang, J.; Cui, Q.; Qiu, S.: A support vector machine for identification of monitors based on their unintended electromagnetic emanation. Progr. Electromagn. Res. M, 30 (2013), 211–224.Google Scholar
[26] Zhou, J.; Duan, B.; Huang, J.: Support-vector modeling of electromechanical coupling for microwave filter tuning. Int. J. RF Microw. Comput. Aided Eng., 23 (1) (2013), 127–139.Google Scholar
[27] Saini, I.; Singh, D.; Khosla, A.: P- and T-wave delineation in ECG signals using support vector machine. IETE J. Res., 59 (5) (2013), 615–623.Google Scholar
[28] Tokan, N.T.; Guneş, F.: Support vector characterization of the microstrip antennas based on measurements. Progr. Electromagn. Res. B, 5 (2008), 49–61.Google Scholar
[29] Guneş, F.; Tokan, N.T.; Gurgen, F.: Support vector design of the microstrip lines. Int. J. RF Microw. Comput. Aided Eng., 18 (4) (2008), 326–336.Google Scholar
[30] Tokan, N.T.; Guneş, F.: Knowledge-based support vector synthesis of the microstrip lines. Progr. Electromagn. Res., 92 (2009), 65–77.Google Scholar
[31] Zheng, Z.; Chen, X.; Huang, K.: Application of support vector machines to the antenna design. Int. J. RF Microw. Comput. Aided Eng., 21 (1) (2011), 85–90.Google Scholar
[32] Ansoft HFSS 10.0, Ansoft Corporation, Pittsburgh, PA.Google Scholar