Hpnna Based Fss Designing: A Case Study
DOI:
https://doi.org/10.26438/ijcse/v6i5.792796Keywords:
ANN, FSS, BPA, HPNNA, PSOAbstract
Soft computing exploits the biological processes to simplify scientific and technical problems. Correspondingly, soft computing is employed in Frequency Selective Surface designing. In this particular endeavor a Back Propagation Algorithm trained Artificial Neural Network is reported for the designing of single layer Frequency Selective Surface. The prime aspiration was to ascertain the Resonant Frequency and the Band Width of a crossed dipole Frequency Selective Surface. In due course of action and to attain maximized throughput latterly a hybrid Particle Swarm Optimization trained Artificial Neural Network Algorithm is formulated. The empirical study confirmed that Hybrid Particle Swarm Optimization trained Artificial Neural Network is amply efficient and effective for global and fast local searching procedures. Afterward a comparative analysis of Hybrid Particle Swarm Optimization and Back Propagation Algorithm is contemplated.
References
[1] B Munk , “Frequency Selective Surfaces: Theory and Design “(NewYork: John Wiley & Sons Inc.),2000.
[2] N. Guerin ; C. Hafner ; X. Cui ; R. Vahldieck, “Compact directive antennas using frequency-selective surfaces (FSS)”,Microwave Conference Proceedings, APMC 2005. Asia-Pacific Conference Proceedings, 2005.
[3] Ortiz, J. D., Baena, J. D., Marques, R., et al., “A band-pass/stop filter made of srrs and c-srrs" in Antennas and Propagation (APSURSI), 2011 IEEE International Symposium on , 2011.
[4] Ghaffer I. Kiani and Rabah W. Aldhaheri, “Wide Band FSS for Increased Thermal and Communication Efficiency in Smart Buildings”, IEEE, 2014.
[5] Woo Cheol Choi, Ki Joon Kim, Young Joong Yoon, “Design of FSS Unit-cell Integrated in Water Bolus for Microwave Biomedical Application” Proceedings of iWEM2014, Sapporo, Japan, 2014.
[6] Chakravarty, S., Mittra, R. and Williams, N. R., “Application of a microgenetic algorithm (mga) to the design of broadband microwave absorbers using multiple frequency selective surface screens buried in dielectrics”, Antennas and Propagation, IEEE Transactions on, Vol. 50no3), pp. 284-296, 2002.
[7] Asim EgemenYILMAZ , Mustafa KUZUOGLU, “Design of the Square Loop Frequency Selective Surfaces with Particle Swarm Optimization via the Equivalent Circuit Model”, RADIO ENGINEERING, vol. 18no2, pp95-101,2009.
[8] M Panda, S Nandi and P P Sarkar, “A comparative study of performance of different back-propagation neural network methods for prediction of resonant frequency of a slot-loaded double-layer frequency-selective surface”, Indian Journal of Physics, 2015.
[9] Mahuya Panda and Partha Pratim Sarkar, “Prediction Of Periodicity Of FSS Structure Using Particle Swarm Optimization”, I-manager’s Journal on Electronics Engineering, vol. 7 no. 3 ,pp25-31, 2017.
[10] Kennedy, J., and Eberhart, R., “Particle Swarm Optimization. IEEE International Conference on Neural Networks; Piscataway pp 1942-1948, 1995.
[11] S Haykin, “Neural Networks, A Comprehensive Foundation” (Englewood Cliffs: Prentice Hall) 2nd edn, 1999.
[12] Jing-Ru Zhang , Jun Zhang, Tat-Ming Lok , Michael R. Lyu, “ A hybrid particle swarm optimization–back-propagation algorithm for Feed forward neural network training”, Applied Mathematics and Computation , Vol. 185,pp1026–1037, 2007
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
