Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 8
2473-2477,2482

Application of sparse regularized inverse neural network in design of dual-notch UWB antenna

Nan Jingchang
Wang Ziqi
Gao Mingming
College of Electrics & Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
DOI: 10.19734/j.issn.1001-3695.2018.02.0109

Abstract

In the design of dula band-notched ultra-wide band antennas, the direct inverse neural network model has lower precision, the BP inverse neural network has poor generalization ability, and if only use the HFSS simulation software, it is necessary to continuously optimize the antenna parameters which increasing the design time. Aiming at the above problems, this paper proposed a method combining HFSS with sparse regularized inverse neural netwrok. This method added l1/2 norm and l2 norm in the performance function of the inverse neural network. The l1/2 norm introduced a new weight coefficient, expanded the input sample vector, made the network to obtain the sparsity solution more easily, and the inverse model got higher accuracy. Meanwhile, the l2 norm avoided the over-fitting phenomenon effectively and made the network generalization ability stronger. It applied to the design of dual band-notched ultra-wide band antenna, using of arc grooves on radiating patches generated notch characteristics, and according to the antenna target voltage standing wave ratio(VSWR) solved inversely the corresponding slot size. Simulation results show that the relative error of slot angle which corresponding to VSWR of the antenna reduced by 69.3%, and the relative error of slot radius lessened by 88.7%, and the network running time decreased by 15.9% compared with BP neural network method. The final designed antenna bandwidth is 2.4~11 GHz, achieves the good notch characteristics in 3.31~3.8 GHz and 4.98~6.05 GHz, and shortens the entire antenna design cycle.

Foundation Support

国家自然科学基金面上项目(61372058)
辽宁省高校重点实验室项目(LJZS007)
辽宁省教育厅科学研究一般项目(L2015209)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.02.0109
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 8
Section: Technology of Information Security
Pages: 2473-2477,2482
Serial Number: 1001-3695(2019)08-049-2473-05

Publish History

[2019-08-05] Printed Article

Cite This Article

南敬昌, 王梓琦, 高明明. 稀疏正则化逆向神经网络在双陷波超宽带天线设计中的应用 [J]. 计算机应用研究, 2019, 36 (8): 2473-2477,2482. (Nan Jingchang, Wang Ziqi, Gao Mingming. Application of sparse regularized inverse neural network in design of dual-notch UWB antenna [J]. Application Research of Computers, 2019, 36 (8): 2473-2477,2482. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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