Semiconductor Optoelectronics, Volume. 45, Issue 6, 971(2024)

Fiber Optic Gyroscope Temperature Compensation and Implementation Based on Particle Swarm Optimization-radial Basis Function Neural Network

QIU Haitao1, FENG Zijian1, and SHI Haiyang2
Author Affiliations
  • 1Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing 100101, CHN
  • 2Beijing Aerospace Times Optoelectronics Technology Co., Ltd., Beijing 100094, CHN
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    References(4)

    [2] [2] Lefvre H C. The Fiber-Optic Gyroscope: Challenges to Become The Ultimate Rotation-Sensing Technology [M]. Optical Fiber Technology, 2013, 19(6): 828-832.

    [3] [3] Jin J, Wang Z, Zhang Z G, et al. Temperature errors modeling for fiber optic gyroscope using multiple linear regression models [J]. J. of Astronautics, 2008, 29(6): 1912-1916.

    [7] [7] Vapnik V. Statistical Learning Theory [M]. New York: Wiley,1998.

    [10] [10] SeoY B, Yu H, Yu M J, et al. Compensation method of gyroscope bias hysteresis error with temperature and rate of temperature using neural networks [C]// 18th Inter. Conf. on Control, Automation and Systems(ICCAS), 2018: 1072-1076.

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    QIU Haitao, FENG Zijian, SHI Haiyang. Fiber Optic Gyroscope Temperature Compensation and Implementation Based on Particle Swarm Optimization-radial Basis Function Neural Network[J]. Semiconductor Optoelectronics, 2024, 45(6): 971

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    Paper Information

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    Received: Jun. 28, 2024

    Accepted: Feb. 28, 2025

    Published Online: Feb. 28, 2025

    The Author Email:

    DOI:10.16818/j.issn1001-5868.2024062801

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