Journal of System Simulation
Abstract
Abstract: To address the issue that changes in ambient temperature significantly affect the output accuracy of the fiber optic gyro (FOG), which causes zero bias drift, increases measurement errors, and limits their application accuracy in complex environments, a temperature compensation model based on BP neural networks was proposed. To improve the performance of neural networks, the sand cat swarm optimization (SCSO) was improved, and the improved SCSO (ISCSO) was used to optimize the weights and thresholds of BP neural networks. Experimental results show that using the ISCSO-BPNN temperature compensation model to compensate for the gyro's temperature errors significantly improves the zero bias stability and overall compensation accuracy compared with other comparative algorithms.
Recommended Citation
Zhang, Zhili; Liu, Jin; Zhou, Zhaofa; Liang, Zhe; and Zhang, Yunhao
(2025)
"Research on Temperature Compensation Technology of Fiber Optic Gyroscope based on ISCSO-BP Neural Network Model,"
Journal of System Simulation: Vol. 37:
Iss.
11, Article 17.
DOI: 10.16182/j.issn1004731x.joss.25-0073
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/17
First Page
2904
Last Page
2917
CLC
TP391.9; TN253; TN29
Recommended Citation
Zhang Zhili, Liu Jin, Zhou Zhaofa, et al. Research on Temperature Compensation Technology of Fiber Optic Gyroscope based on ISCSO-BP Neural Network Model[J]. Journal of System Simulation, 2025, 37(11): 2904-2917.
DOI
10.16182/j.issn1004731x.joss.25-0073
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