摘要
针对光纤陀螺(FOG)随温度呈非线性变化的特性,提出了采用BP神经网络对刻度因子的温度误差建模的方法,以减小光纤陀螺输出误差;用BP网络对其建模的结果和传统的建模结果进行了比较,结果表明采用BP神经网络对刻度因子的建模是非常有效的。
Pointing to the non-linearity of the FOG with the change of temperature, a method of modeling for the temperature error of scale factor with BP neural network is proposed, and it can reduce the output error of fiber optic gyroscope. Meanwhile the modeling results of BP neural network for scale factor are compared with the traditional modeling results, it demonstrates that modeling for scale factor with BP neural network is very effective.
出处
《计测技术》
2006年第2期19-20,31,共3页
Metrology & Measurement Technology
关键词
光纤陀螺
刻度因子
BP网络
建模
fiber optical gyro
scale factor
BP network, modeling
