Fast Multi-objective Antenna Design Based on Improved Back Propagation Neural Network Surrogate Model

Jian DONG; Wenwen QIN; Yingjuan LI; Qianqian LI; Lianwen DENG

doi:10.11999/JEIT180025

Volume 40 Issue 11

Oct. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2712-2719

Jian DONG, Wenwen QIN, Yingjuan LI, Qianqian LI, Lianwen DENG. Fast Multi-objective Antenna Design Based on Improved Back Propagation Neural Network Surrogate Model[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2712-2719. doi: 10.11999/JEIT180025

Citation:

Jian DONG, Wenwen QIN, Yingjuan LI, Qianqian LI, Lianwen DENG. Fast Multi-objective Antenna Design Based on Improved Back Propagation Neural Network Surrogate Model[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2712-2719. doi: 10.11999/JEIT180025

Citation:

PDF( 1853 KB)

Fast Multi-objective Antenna Design Based on Improved Back Propagation Neural Network Surrogate Model

doi: 10.11999/JEIT180025

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China
2.
School of Physics and Electronics, Central South University, Changsha 410083, China

Funds: The National Key Research and Development Program of China (2017YFA0204600), The Natural Science Foundation of Hunan Province (2018JJ2533)

Received Date: 2018-01-08
Rev Recd Date: 2018-07-17

Available Online: 2018-07-30

Publish Date: 2018-11-01

Abstract

Abstract

Focusing on the problem of reducing the large computation cost of traditional antenna design methods, a new surrogate model based on Back Propagation Neural Networks (BPNN) is constructed. In order to solve the problem of easily falling into local optimum in BPNN, a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures. The design results show that the proposed PSO-BPNN outperforms other existing antenna surrogate models in terms of prediction accuracy and prediction speed. The proposed method is of value in dealing with complex antenna designs with high-dimensional parameter space.
- Antenna design,
- Performance prediction,
- Surrogate model,
- Back Propagation Neural Network (BPNN),
- Particle Swarm Optimization (PSO)

FullText(HTML)

References(21)

References

MOHAMMED H J, ABDULLAH, A S, ALI R S, et al. Design of a uniplanar printed triple band-rejected ultra-wideband antenna using particle swarm optimisation and the firefly algorithm[J]. IET Microwaves,Antennas&Propagation, 2016, 10(1): 31–37 doi: 10.1049/iet-map.2014.0736

CHOI K, JANG D, KANG S, et al. Hybrid algorithm combing genetic algorithm with evolution strategy for antenna design[J]. IEEE Transactions on Magnetics, 2016, 52(3): 7209004 doi: 10.1109/TMAG.2015.2486043

GOUDOS S K, KALIALAKIS C, and MITTRA R. Evolutionary algorithms applied to antennas and propagation: A review of state of the art[J]. International Journal of Antennas and Propagation, 2016, 2016(4): 1–12 doi: 10.1155/2016/1010459

KOZIEL S and OGURTSOY S. Multi-objective design of antennas using variable-fidelity simulations and surrogate models[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(12): 5931–5939 doi: 10.1109/TAP.2013.2283599

陈晓辉, 裴进明, 郭欣欣, 等. 一种基于多维均匀采样与Kriging模型的天线快速优化方法[J]. 电子与信息学报, 2014, 36(12): 3021–3026 doi: 10.3724/SP.J.1146.2013.01826

CHEN Xiaohui, PEI Jinming, GUO Xinxin, et al. An efficient antenna optimization method based on kriging model and multidimensional uniform sampling[J]. Journal of Electronics&Information Technology, 2014, 36(12): 3021–3026 doi: 10.3724/SP.J.1146.2013.01826

DONG Jian, LI Qianqian, and DENG Lianwen. Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model[J]. AEUE-International Journal of Electronics and Communications, 2017, 72: 192–199 doi: 10.1016/j.aeue.2016.12.007

LIU Bo, ALIAKBARIAN H, MA Zhongkun, et al. An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(1): 7–18 doi: 10.1109/TAP.2013.2283605

JACOBS J P. Efficient resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression[J]. IEEE Antennas and Wireless Propagation Letters, 2015, 14: 337–341 doi: 10.1109/LAWP.2014.2362937

CHEN Linglu, LIAO Cheng, LIN Wenbin, et al. Hybrid-surrogate-model-based efficient global optimization for high-dimensional antenna design[J]. Progress in Electromagnetics Research, 2012, 124(8): 85–100 doi: 10.2528/PIER11121203

MASSA A, OLIVERI G, SALUCCI M, et al. Learning-by-examples techniques as applied to electromagnetics[J]. Journal of Electromagnetic Waves and Applications, 2017, 32(4): 516–541 doi: 10.1080/09205071.2017.1402713

焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年: 回顾与展望[J]. 计算机学报, 2016, 39(8): 1697–1716 doi: 10.11897/SP.J.1016.2016.01697

JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Seventy years beyond neural networks: retrospect and prospect[J]. Chinese Journal of Computers, 2016, 39(8): 1697–1716 doi: 10.11897/SP.J.1016.2016.01697

公茂果, 焦李成, 杨咚咚, 等. 进化多目标优化算法研究[J]. 软件学报, 2009, 20(2): 271–289 doi: 10.3724/SP.J.1001.2009.03483

GONG Maoguo, JIAO Licheng, YANG Dongdong, et al. Research on evolutionary multi-objective optimization algorithms[J]. Journal of Software, 2009, 20(2): 271–289 doi: 10.3724/SP.J.1001.2009.03483

RUMELHART D E, HINTON G E, and WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(9): 533–536 doi: 10.1038/323533a0

KOLMOGOROV A N. On the representation of continuous functions of several variables by superposition of continuous functions of one variable and addition[J]. Doklady Akademii Nauk SSSR, 1957, 114(5): 953–956 doi: 10.1007/978-94-011-3030-1_56

STEIN M. Large sample properties of simulations using Latin hypercube sampling[J]. Technometrics, 1987, 29(2): 143–151 doi: 10.1080/00401706.1987.10488205

KENNEDY J and EBERHART R C. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, 1995, 4: 1942–1948. doi: 10.1109/icnn.1995.488968.

COELLO C A C, PULIDO G T, and LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256–279 doi: 10.1109/TEVC.2004.826067

DONG Jian, YU Xiaping, and HU Guoqiang. Design of a compact quad-band slot antenna for integrated mobile devices[J]. International Journal of Antennas and Propagation, 2016, 2016: 1–9 doi: 10.1155/2016/3717681

ANURDHA, PATNAIK A, and SINHA S N. Design of custom-made fractal multi-band antennas using ANN-PSO[J]. IEEE Antennas&Propagation Magazine, 2011, 53(4): 94–101 doi: 10.1109/MAP.2011.6097296

ROBINSON J and RAHMAT-SAMMI Y. Particle swarm optimization in electromagnetics[J]. IEEE Transactions on Antennas and Propagation, 2004, 52(2): 397–407 doi: 10.1109/TAP.2004.823969

JIN Nanbo and RAHMAT-SAMMI Y. Advances in particle swarm optimization for antenna designs: Real-number, binary, single-objective and multiobjective implementations[J]. IEEE Transactions on Antennas and Propagation, 2007, 55(3): 556–567 doi: 10.1109/TAP.2007.891552

Relative Articles

Supplements(0)

Cited By

Proportional views