基于BAS-BP-Bagging模型的光纤陀螺温度补偿

The Temperature Compensation Method of Fiber Optic Gyroscope Based on BAS-BP-Bagging Neural Network

摘要:

为提高光纤陀螺的输出精度,以天牛须搜索算法(BAS)优化后的BP神经网络模型为基学习器,采用Bagging并行集成学习算法建立了BAS-BP-Bagging温度补偿模型,并对某型号光纤陀螺进行了温度补偿实验。实验结果表明,在-40℃～+60℃温变环境下,该方法补偿后的光纤陀螺温度漂移相较于补偿前减小了近80%,相较于多项式补偿算法减小了55%,相较于BP神经网络补偿算法减小了30%左右。同时该模型在对新鲜样本的补偿过程中表现出了较为优越的泛化性能。

Abstract:

In order to improve the output accuracy of fiber optic gyroscope, a BAS-BP-Bagging temperature compensation model is established by using a BP neural network model optimized by the beetle antennae search algorithm (BAS) as the base learner and a Bagging parallel ensemble learning algorithm, and a temperature compensation experiment is conducted for a model of fiber optic gyroscope. The experimental results show that the temperature drift of the fiber optic gyroscope after compensation is reduced by nearly 80% compared with that before compensation, 55% compared with the polynomial compensation algorithm, and about 30% compared with the BP neural network compensation algorithm under the temperature change environment from -40℃ to +60℃. And the model shows superior generalization performance in the compensation of fresh samples.