基于KPCA和数据处理组合方法神经网络的半球谐振陀螺温度建模补偿方法

张晨,汪立新,孔祥玉

Temperature modeling and compensation method of hemispherical resonator gyro based on KPCA and grouped method of data handling neural network

Chen ZHANG,Lixin WANG,Xiangyu KONG

表 2 样本经核主成分分析筛选后的特征向量统计特性

Tab.2 Statistical characteristics of feature vectors for samples after kernel principal component analysis

特征 最大值 最小值 平均值 ${{\boldsymbol{X}}_1}$ 18.6932 −95.1230 2.600 9×10−14 ${{\boldsymbol{X}}_2}$ 9.5840 −14.0542 −7.9554×10−13 ${{\boldsymbol{X}}_3}$ 6.1600 −10.3414 −1.988 5×10−13