Application of neural network in temperature compensation of FOG scale factor

被引：0
|
作者：
Chen, Yong-Qi [1 ]
Zhang, Chun-Xi [1 ]
Zhu, Kui-Bao [1 ]
机构：
[1] School of Instrument Science and Opt-electronic Engineering, Beihang University, Beijing 100083, China
来源：
Yadian Yu Shengguang/Piezoelectrics and Acoustooptics | 2007年 / 29卷 / 05期
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摘要：
The scale factor of fiber optic gyro is influenced greatly by temperature. This text carried on the analysis about the reason, including the length of fiber, the diameter of fiber coil, middle wavelength, the modulation coefficient of integrated optical modulator and 2π reset voltage. The temperature model compensation was researched deeply. The traditional least-two-multiply method had some disadvantage on solving this problem. BP neural network compensation method was brought forward to this issue. Through the analysis and comparison between these methods, the simulation results indicated that the error reduced greatly by using BP neural network method, which guaranteed actual gyro resolution in inertia navigation system in full temperature scale.
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页码：516 / 518
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