PSO优化SVM的MEMS陀螺温度零偏补偿

Temperature Compensation of MEMS-Gyro Based on Particle Swarm Optimization and Support Vector Machines

针对微机械陀螺零偏受温度影响较大的问题,提出一种粒子群优化(PSO)算法和支持向量机(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,并进行线性拟合,保证泛化能力。然后,利用粒子群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PSO调节支持向量机算法补偿后,陀螺输出精度更高;与最小二乘法、BP神经网络法相比,陀螺输出数据方差分别减小了81.3%和57%,最大误差分别减小54.7%和48.5%。

Aiming at the problem that bias of MEMS gyroscope is severely affected by temperature,a temperature compensation method based on Particle Swarm Optimization(PSO)and Support Vector Machine(SVM)is proposed.Firstly,the smoothed gyroscope data is taken as the sample point,and the drift model is constructed by the SVM method based on radial basis kernel function.The data is mapped from low-dimensional space to high-dimensional space for linear fitting to ensure generalization ability.Then,the PSO algorithm is used to optimize the penalty parameters,kernel function parameters and bias parameters of the SVM,which avoids the blindness of artificial parameter choosing and improves the accuracy of the model.Experimental results show that gyro output accuracy is higher after PSO-adjusted SVM compensation.Compared with the least squares method and the BP neural network method,the variance of the gyro output data is reduced by 81.3%and 57%respectively,and the maximum error is reduced by 54.7%and 48.5%respectively.