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

  • Chandan Roy (a1), Taimoor Khan (a1) and Binod Kumar Kanaujia (a2)


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.


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Corresponding author: T. Khan Email:


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[1] Bahl, I.J.; Bhartia, P.: Microstrip Antennas, Artech House, Dedham, MA, 1980.
[2] Garg, R.; Bhartia, P.; Bahl, I.; Ittipiboon, A.: Microstrip Antenna Design Handbook, Artech House, Boston, 2001.
[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), 13671371.
[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.
[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), 673680.
[6] Khan, T.; De, A.: Modeling of microstrip antennas using neural network techniques: a review. Int. J. RF Microw. Comput. Aided Eng., 25 (2015), 747757.
[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), 2332.
[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), 6065.
[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), 146154.
[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), 659668.
[11] Bose, T.; Gupta, N.: Design of an aperture-coupled microstrip antenna using a hybrid neural network. IET Microw., Antennas Propag., 6 (4) (2012), 470474.
[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), 59895992.
[13] Vapnik, V.N.: The Nature of Statistical Learning Theory, Springer–Verlag, New York, 1995.
[14] Hearst, M.A.; Dumais, S.T.; Osman, E.; Platt, J.: Support vector machines. IEEE Intell. Syst. Appl., 13 (4) (1998), 1828.
[15] Mattera, D.; Palmieri, F.; Haykin, S.: An explicit algorithm for training support vector machines. IEEE Signal Process. Lett., 6 (9) (1999), 243245.
[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), 191202.
[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, 671675.
[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), 12211231.
[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), 33023309.
[20] Wang, F.F.; Zhang, Y.R.: The support vector machine for dielectric target detection through a wall. Progr. Electromagn. Res. Lett., 23 (2011), 119128.
[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, 807812.
[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), 11981211.
[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), 687692.
[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), 7181.
[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), 211224.
[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), 127139.
[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), 615623.
[28] Tokan, N.T.; Guneş, F.: Support vector characterization of the microstrip antennas based on measurements. Progr. Electromagn. Res. B, 5 (2008), 4961.
[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), 326336.
[30] Tokan, N.T.; Guneş, F.: Knowledge-based support vector synthesis of the microstrip lines. Progr. Electromagn. Res., 92 (2009), 6577.
[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), 8590.
[32] Ansoft HFSS 10.0, Ansoft Corporation, Pittsburgh, PA.


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

  • Chandan Roy (a1), Taimoor Khan (a1) and Binod Kumar Kanaujia (a2)


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