Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases. 1. Introduction Microstrip antennas are being widely used for different applications in wireless communication due to several attractive features: low profile, conformable to planar, and nonplanar surfaces, most economical, mechanically robust, light weight, easy mount-ability, and so forth. [1]. Since the microstrip antenna (MSA) operates only in the vicinity of resonance frequency, it needs to be calculated accurately for analyzing the microstrip antennas. Similarly, for designing the MSAs, the physical dimensions must also be calculated precisely. Several classical methods [2–14] have been used for computing the resonance frequency of rectangular MSAs [2–5], resonance frequency of circular MSAs [6–12], and resonance frequency of triangular MSAs [13, 14]. These methods can broadly be categorized as analytical methods and numerical methods. The analytical methods provide a good spontaneous explanation for the operation of MSAs. These methods are based on the physical assumptions for simplifying the radiation mechanism of the MSAs and are not suitable for many structures where the thickness of the substrate is not very thin. On the other hand, the numerical methods provide accurate results but only at the cost of huge mathematical burden in the form of complex integral equations. The choice of test function and path integration appears to be more critical without initial assumptions in the final stage of the numerical results. Also, these methods require a new solution even for a minor alteration in the geometry. Thus, the requirement for having a new solution for every minor change in the geometry as well
References
[1]
I. J. Bahl and P. Bhartia, Microstrip Antennas, Artech House, Dedham, Mass, USA, 1980.
[2]
E. Chang, S. A. Long, and W. F. Richards, “An experimental investigation of electrically thick rectangular microstrip antennas,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 6, pp. 767–772, 1986.
[3]
K. R. Carver, “Practical analytical techniques for the microstrip antennas,” in Proceedings of Workshop on Printed Circuit Antenna Technology, pp. 7.1–7.20, New Mexico State University, Las Cruces, Mexico, 1979.
[4]
M. Kara, “The resonant frequency of rectangular microstrip antenna elements with various substrate thicknesses,” Microwave and Optical Technology Letters, vol. 11, no. 2, pp. 55–59, 1996.
[5]
M. Kara, “Closed-form expressions for the resonant frequency of rectangular microstrip antenna elements with thick substrates,” Microwave and Optical Technology Letters, vol. 12, no. 3, pp. 131–136, 1996.
[6]
J. S. Dahele and K. F. Lee, “Effect of substrate thickness on the performance of a circular-disk microstrip antennas,” IEEE Transactions on Antennas and Propagation, vol. 31, no. 2, pp. 358–360, 1983.
[7]
J. S. Dahele and K. F. Lee, “Theory and experiment on microstrip antennas with air-gaps,” in Proceedings of IEE Conference, vol. 132, pp. 455–460, 1985.
[8]
K. R. Carver, “Practical analytical techniques for the microstrip antennas,” in Proceedings of Workshop on Printed Circuit Antennas, New Mexico State University, 1979.
[9]
K. Antoskiewicz and L. Shafai, “Impedance characteristics of circular microstrip patches,” IEEE Transactions on Antennas and Propagation, vol. 38, no. 6, pp. 942–946, 1990.
[10]
J. Q. Howell, “Microstrip antennas,” IEEE Transactions on Antennas and Propagation, vol. 23, no. 1, pp. 90–93, 1975.
[11]
T. Itoh and R. Mittra, “Analysis of a microstrip disk resonator,” Archiv fur Elektronik und Ubertragungstechnik, vol. 27, no. 11, pp. 456–458, 1973.
[12]
F. Abboud, J. P. Damiano, and A. Papiernik, “New determination of resonant frequency of circular disc microstrip antenna: application to thick substrate,” Electronics Letters, vol. 24, no. 17, pp. 1104–1106, 1988.
[13]
W. Chen, K. F. Lee, and J. S. Dahele, “Theoretical and experimental studies of the resonant frequencies of the equilateral triangular microstrip antennas,” IEEE Transactions on Antennas and Propagation, vol. 40, pp. 1253–1256, 1992.
[14]
J. S. Dahele and K. F. Lee, “On the resonant frequencies of the triangular patch antennas,” IEEE Transactions on Antennas and Propagation, vol. 35, no. 1, pp. 100–101, 1987.
[15]
P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, Academic Press, New York, NY, USA, 1981.
[16]
L. E. Scales, Introduction to Non-Linear Optimization, Springer, New York, NY, USA, 1985.
[17]
M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994.
[18]
D. Karaboga, K. Guney, S. Sagiroglu, and M. Erler, “Neural computation of resonant frequency of electrically thin and thick rectangular microstrip antennas,” IEE Proceedings, vol. 146, no. 2, pp. 155–159, 1999.
[19]
K. Guney, S. Sagiroglu, and M. Erler, “Generalized neural method to determine resonant frequencies of various microstrip antennas,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 12, no. 1, pp. 131–139, 2002.
[20]
K. Guney and N. Sarikaya, “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 Transactions on Antennas and Propagation, vol. 55, no. 3, pp. 659–668, 2007.
[21]
K. Guney and N. Sarikaya, “Concurrent neuro-fuzzy systems for resonant frequency computation of rectangular, circular, and triangular microstrip antennas,” Progress in Electromagnetics Research, vol. 84, pp. 253–277, 2008.
[22]
N. Türker, F. Güne？, and T. Yildirim, “Artificial neural design of microstrip antennas,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 14, no. 3, pp. 445–453, 2006.
[23]
S. Sagiroglu, K. Güney, and M. Erler, “Resonant frequency calculation for circular microstrip antennas using artificial neural networks,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 8, no. 3, pp. 270–277, 1998.
[24]
Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, 2000.
[25]
P. M. Watson and K. C. Gupta, “EM-ANN models for microstrip vias and interconnects in dataset circuits,” IEEE Transactions on Microwave Theory and Techniques, vol. 44, no. 12, pp. 2495–2503, 1996.
[26]
P. M. Watson and K. C. Gupta, “Design and optimization of cpw circuits using em-ann models for cpw components,” IEEE Transactions on Microwave Theory and Techniques, vol. 45, no. 12, pp. 2515–2523, 1997.
[27]
P. M. Watson, K. C. Gupta, and R. L. Mahajan, “Development of knowledge based artificial neural network models for microwave components,” IEEE International Microwave Digest, vol. 1, pp. 9–12, 1998.
[28]
S. Sagiroglu and K. Güney, “Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks,” Microwave and Optical Technology Letters, vol. 14, no. 2, pp. 89–93, 1997.
[29]
R. Gopalakrishnan and N. Gunasekaran, “Design of equilateral triangular microstrip antenna using artifical neural networks,” in Proceedings of the IEEE International Workshop on Antenna Technology (IWAT '05), pp. 246–249, March 2005.
[30]
T. Khan and A. De, “Computation of different parameters of triangular patch microstrip antennas using a common neural model,” International Journal of Microwave and Optical Technology, vol. 5, pp. 219–224, 2010.
[31]
T. Khan and A. De, “A common neural approach for computing different parameters of circular patch microstrip antennas,” International Journal of Microwave and Optical Technology, vol. 6, no. 5, pp. 259–262, 2011.
[32]
S. Chen, C. F. N. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks,” IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 302–309, 1991.
[33]
H. Demuth and M. Beale, Neural Network Tool Box for Use with MATLAB, User’s Guide, The Math Works Inc, 5th edition, 1998.
