基于ACO-BP神经网络的光纤陀螺温度补偿方法研究

Research on Temperature Compensation Method of Fiber Optic Gyroscope Based on ACO-BP Neural Network

温度变化对光纤陀螺零偏的影响是制约其性能的关键因素之一,采用BP神经网络进行预测能在一定程度上提高温度补偿精度,但BP神经网络存在局部极小的问题。采用蚁群优化(ACO)BP神经网络算法补偿光纤陀螺漂移,优化了BP神经网络的初始参数。实验结果表明,采用ACO-BP神经网络进行补偿,可使得在-40℃~60℃温度范围内光纤陀螺零偏稳定性比补偿前有80%左右的精度提升,与以往的BP神经网络效果相比,补偿效果更好。

The influence of temperature change on the zero offset of optical fiber gyro is one of the key factors that restricting its performance.The BP neural network can improve the accuracy of temperature compensation to a certain extent,but the BP neural network has local minimum problem.In this paper,Ant Colony Optimization(ACO)BP neural network algorithm is used to compensate the drift of fiber optic gyro,and the initial parameters of BP neural network are optimized.The experimental results show that using ACO-BP neural network to compensate can improve the zero offset stability of fiber optic gyro by about 80% in the temperature range of -40℃~60℃,and the compensation effect is better than that of previous BP neural network.