改进布谷鸟搜索算法优化支持向量机的MEMS陀螺温度零偏补偿

针对微机械陀螺零偏受温度影响较大的问题，提出一种改进布谷鸟搜索算法(CS)和支持向量机(SVM)相结合的陀螺零偏温度补偿方法。首先，将平滑处理后的陀螺数据作为样本点，采用基于径向基核函数的支持向量机方法构建漂移模型，把数据从低维空间映射到高维空间进行线性拟合。然后，利用改进布谷鸟算法对支持向量机的惩罚参数、核函数参数以及不敏感系数进行优化，避免了人为选择参数的盲目性且提高了建立模型的精度。实验结果表明：经CS调节支持向量机算法补偿后，陀螺输出精度更高。与最小二乘分段拟合方法、BP神经网络方法相比，陀螺输出数据方差分别平均减小了63.2%、43.4%，最大误差分别平均减小71.63%、48.3%。

Temperature Compensation of MEMS Gyro Based on Improving Cuckoo Search and Support Vector Machines

 Aiming at the problem that the zero bias of an MEMS gyro is greatly influenced by temperature, a gyro zero bias temperature compensation method based on the combination of the cuckoo search (CS) algorithm and the support vector machine (SVM) is proposed. Firstly, the smoothed gyro data is taken as the sample point. The SVM method based on the radial basis kernel function is used to construct the drift model, and the data is mapped from the low dimensional space to the high dimensional space for linear fitting. Then, the cuckoo algorithm is used to optimize the penalty vector, kernel function parameters and insensitivity coefficient of the SVM, avoid the blindness of the artificially selected parameters, and improve the accuracy of the established model. The experimental results show that the gyro output accuracy is higher after CS compensation and vector machine algorithm compensation. Compared with the least-squares segmented fitting method and the BP neural network method, the gyro output data variances decrease by 63.2% and 43.4%, respectively, and the maximum errors decrease by an average of 71.63% and 48.3%, respectively.