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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


Guanghua Li1, Hao Zhang1, Shitong Wang2

Corresponding Author:
Guanghua Li

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

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


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.


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.


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