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GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

Ye Aifen et al.

International Journal of Information Technology and Web Engineering2024https://doi.org/10.4018/ijitwe.337491article
ABDC C
Weight
0.46

What the paper says

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.

4 citations

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https://doi.org/https://doi.org/10.4018/ijitwe.337491

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@article{ye2024,
  title        = {{GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation}},
  author       = {Ye Aifen et al.},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.337491},
}

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GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.