Abstract
Artificial neural network (ANN) is widely used for modeling and optimization in antenna design problems. It is a very convenient alternative for using computationally intensive 3D-Electromagnetic (EM) simulation in design. The reconfigurable microstrip patch antennas have been considered to ensure operational frequencies for different kind of purposes. ANN is used for modeling of antenna design problems to obtain a surrogate based model instead of a computationally intensive 3D-EM simulation. Further improvement in modeling, a prior knowledge about the problem such as an empirical formula, an equivalent circuit model, and a semi-analytical equation is directly embedded in ANN structure through a knowledge based modeling technique. Knowledge based techniques are developed to improve some properties of conventional ANN modeling such as accuracy and data requirement. All these improvements ensure better accuracy compared to conventional ANN modeling. The necessary knowledge can be obtained by the coarse model which is a complex 3D-EM simulation in terms of grid size selection. Knowledge based techniques can improve the performance of conventional ANN through the guidance of the coarse model. As long as the coarse model approximates to the computationally intensive 3D-EM simulation, the performance of the knowledge based surrogate model can converge to the design targets. The efficiency of modeling strategies is demonstrated by a reconfigurable 5-fingers microstrip patch antenna. The antenna has four modes of operation, which are controlled by two PIN diode switches with ON/OFF states, and it resonates at multiple frequencies between 1 and 7 GHz. The number of training data is changed in terms of selected parameters from the design space. Three different sets are used to show modeling performance according to the size of training data. The simulation results show that knowledge based neural networks ensure considerable savings in computational costs as compared to the computationally intensive 3D-EM simulation while maintaining the accuracy of the fine model.
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References
Aoad, A., Aydin, Z., Korkmaz, E.: Design of a tri band 5-fingers shaped microstrip patch antenna with an adjustable resistor. In: Antenna Measurements Applications (CAMA), pp. 1–4 (2014)
Aoad, A., Simsek, M., Aydin, Z.: Design of a reconfigurable 5-fingers shaped microstrip patch antenna by artificial neural networks. Int. J. Adv. Res. Comput. Sci. Softw. Eng. (IJARCSSE) 4(10), 61–70 (2014)
Bandler, J.W., Cheng, Q.S., Dakroury, S.A., Mohamed, A.S., Bakr, M.H., Madsen, K., Sondergaard, J.: Space mapping: The state of the art. IEEE Trans. Microwave Theory Tech. 52(1), 337–361 (2004)
Burrascano, P., Fiori, S., Mongiardo, M.: A review artificial neural networks applications in microwave computer-aided design. Int. J. RF Microwave Comput.-Aided Eng. 9(3), 158–174 (1999)
Costantine, J.: Design, optimization and analysis of reconfigurable antennas. Ph.D. thesis, Electrical and Electronics Engineering, The University of New Mexico, Albuquerque, New Mexico (2009)
Devabhaktuni, V.K., Chattaraj, B., Yagoub, M.C.E., Zhang, Q.J.: Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping. IEEE Trans. Microw. Theory Tech. 51(7), 1822–1833 (2003)
Haykin, S.: Neural Network - A Comprehensive Foundation, 2nd edn. Printice Hall Inc., New Jersey (1999)
Huff, G.H., Bernhard, J.T.: Reconfigurable antennas. In: Modern Antenna Handbook, pp. 369–398. Wiley, Inc., New York (2007)
Koziel, S., Bandler, J.W.: Modeling of microwave devices with space mapping and radial basis functions. Int. J. Numer. Model: Electron. Networks, Devices Fields 21(1-2), 187–203 (2008)
Patnaik, A., Anagnostou, D., Christodoulou, C.G., Lyke, J.C.: A frequency reconfigurable antenna design using neural networks. In: Antennas and Propagation Society International Symposium, vol. 2A, pp. 409–412 (2005)
Rayas-Sanchez, J.E.: Em-based optimization of microwave circuits using artificial neural networks: the state-of-the-art. IEEE Trans. Microwave Theory Tech. 52(1), 420–435 (2004)
Simsek, M.: Developing 3-step modeling strategy exploiting knowledge based techniques. In: The 20th European Conference on Circuit theory and Design, Linkoping, Sweden, Aug 29-31 (2011)
Simsek, M.: Knowledge based three-step modeling strategy exploiting artificial neural network. In: Koziel, S., Leifsson, L., X.-S. Yang, (eds.) Solving Computationally Expensive Engineering Problems, volume 97 of Springer Proceedings in Mathematics Statistics, pp. 219–239. Springer International Publishing, New York (2014)
Simsek, M., Sengor, N.S.: A knowledge-based neuromodeling using space mapping technique: compound space mapping-based neuromodeling. Int. J. Numer. Model: Electron. Networks, Devices Fields 21(1-2), 133–149 (2008)
Simsek, M., Sengor, N.S.: An efficient inverse ANN modeling approach using prior knowledge input with difference method. In: The European Conference on Circuit theory and Design, Antalya, Turkey, Aug 23 to 27 (2009)
Simsek, M., Sengor, N.S.: The efficiency of difference mapping in space mapping-based optimization. In: Koziel, S., Leifsson, L. (eds.) Surrogate-Based Modeling and Optimization, pp. 99–120. Springer, New York (2013)
Simsek, M., Serap Sengor, N.: Solving inverse problems by space mapping with inverse difference method. In: Roos, J., Costa, L.R.J. (eds.) Scientific Computing in Electrical Engineering SCEE 2008, Mathematics in Industry, pp. 453–460. Springer, Berlin/ Heidelberg (2010)
Simsek, M., Tezel, N.S.: The reconstruction of shape and impedance exploiting space mapping with inverse difference method. IEEE Trans. Antennas Propag. 60(4), 1868–1877 (2012)
Simsek, M., Zhang, Q.J., Kabir, H., Cao, Y., Sengor, N.S.: The recent developments in microwave design. Int. J. Math. Modelling and Numer. Optim. 2(2), 213–228 (2011)
Sondergard, J.: Optimization using surrogate models by the space mapping technique. Ph.D. thesis, Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark, Jan (2003)
Watson, P.M., Gupta, K.C.: EM-ANN models for microstrip vias and interconnects in multilayer circuits. IEEE Trans. Microw. Theory Tech. 44, 2495–2503 (1996)
Watson, P.M., Gupta, K.C., Mahajan, R.L.: Development of knowledge based artificial neural network models for microwave components. In: International Microwave Symposium Digest, pp. 9–12. IEEE, New York (1998)
Zhang, Q.J., Gupta, K.C.: Neural Networks for RF and Microwave Design. Artech House, Boston (2000)
Zhang, Q.J., Gupta, K.C., Devabhaktuni, V.K.: Artificial neural networks for RF and microwave design - from theory to practice. IEEE Trans. Microwave Theory Tech. 51(4), 1339–1350 (2003)
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Simsek, M., Aoad, A. (2016). Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks. In: Koziel, S., Leifsson, L., Yang, XS. (eds) Simulation-Driven Modeling and Optimization. Springer Proceedings in Mathematics & Statistics, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-319-27517-8_8
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