光纤陀螺
补偿(心理学)
计算机科学
均方误差
期限(时间)
理论(学习稳定性)
控制理论(社会学)
光纤
人工智能
统计
机器学习
数学
电信
精神分析
物理
量子力学
控制(管理)
心理学
作者
Yin Cao,Wenyuan Xu,Bo Lin,Yunmin Zhu,Fanchao Meng,Xiaoting Zhao,Jialuo Ding,Shuqin Lou,Xin Wang,Jiasong He,Xinzhi Sheng,Sheng Liang
出处
期刊:Applied Optics
[The Optical Society]
日期:2022-09-21
卷期号:61 (28): 8212-8212
被引量:1
摘要
We present an artificial intelligence compensation method for temperature error of a fiber optic gyroscope (FOG). The difference from the existing methods is that the compensation model finally determined by this method only uses the FOG's data to complete the regression prediction of the temperature error and eliminate the dependency on the temperature sensor. In the experimental stage, the proposed method performs temperature experiments with three varying trends of temperature heating, holding, and cooling and obtains sufficient output data sets of the FOG. Taking the output time series of the FOG as the input sample and based on the long short-term memory network of machine learning, the training, validation, and test of the model are completed. From the two perspectives of network learning ability and the improvement degree of the FOG's performance, four indicators, including root mean square error, error cumulative distribution function, FOG bias stability, and Allan variance analysis are selected to evaluate the performance of the compensation model comprehensively. Compared with the existing methods using temperature information for prediction and compensation, the results show that the error compensation method without temperature information proposed can effectively improve the accuracy of the FOG and reduce the complexity of the compensation system. The work can also provide technical references for error compensation of other sensors.
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