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Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050612.

Train a correct BP neural network as the input layer and as the training sample

Author(s)

Guanghua Li1, Hao Zhang1, Shitong Wang2

Corresponding Author:
Guanghua Li
Affiliation(s)

1College of Science, Tibet University, Lhasa 850000, Tibet, China

2College of Technology, Tibet University, Lhasa 850000, Tibet, China

Abstract

For this problem, we train a correct BP neural network with the number of layers, material and thickness of each layer, emission spectrum pollution index as the input layer, as the training sample, and comprehensive thermal efficiency as the output layer, to obtain the network mapping relationship between comprehensive thermal efficiency and design parameters of multilayer structure. Then, through particle swarm optimization algorithm and genetic algorithm, six layers are obtained, which adopt silica silicon silica germanium silicon germanium structure, with refractive index of 21.7832 and thickness of 130.819. Currently, the corresponding thermoelectric conversion efficiency is the highest.

Keywords

BP neural network model, input layer, training sample

Cite This Paper

Guanghua Li, Hao Zhang, Shitong Wang. Train a correct BP neural network as the input layer and as the training sample. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 83-88. https://doi.org/10.25236/AJCIS.2022.050612.

References

[1] Wang H, Alshehri H, Su H, et al. Design, fabrication and opticalcharacterizations of large-area  lithography-free  ultrathin  multilayer  selective  solar  coatings  with  excellent  thermal stability in air[J]. Solar Energy Materials and Solar Cells, 2018, 174: 445-452. 

[2] Lenert A, Bierman D M, Nam Y, et al. A nanophotonic solar thermophotovoltaic device[J]. Nature nanotechnology, 2014, 9(2): 126-130. 

[3] Zhou Jingbo, Wang Kai, Yang Guang, Liu Xiaohong, Du Renjie, Li Yuehua. Real-time uncertainty estimation of stripe center extraction results using adaptive BP neural network[J]. Measurement, 2022,194.

[4] Wu Qiang, Wang Yu, Wang Yaoping, Wang Jian, Lan Lei, Deng Yeqiang, Wen Xishan, Luo Bing, Xiao Wei. Ablation state assessment of SF6 circuit breaker contacts based on BP neural network and mean impact value[J]. Energy Reports, 2022,8(S5).

[5] Jinlong Wang, Wenjie Peng, Yongjie Bao, Yuxing Yang, Chen Chen. VHCF evaluation with BP neural network for centrifugal impeller material affected by internal inclusion and GBF region[J]. Engineering Failure Analysis, 2022,136.

[6] Xu Bin, Yuan Xiang. A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China[J]. Sustainability, 2022,14(4).

[7] Zhu Haoliang, Wang Yousong. Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network[J]. Applied Sciences, 2022,12(3).

[8] Kun Liu. The Prediction Model and System of Stock Rise and Fall Based on BP Neural Network[J]. Academic Journal of Business & Management, 2022,4.0(1.0).