Estimation of GM (1, 1) model parameter usually adopts the least square criterion, but test of model precision often USES average relative error criterion.
估计GM(1,1)模型中的参数通常采用最小二乘准则，而在模型精度检验时又常采用平均相对误差。
The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.
对SVM多元非线性回归泛化性能进行测试，其均方根相对误差为1.06%，平均绝对相对误差为0.96%，最大绝对相对误差为1.16%。
Scatter diagrams and the statistical criteria of relative error and mean square deviation were used to evaluate this model.
通过散点图以及相对误差、均方差两种统计学指标对该模型进行评价。
Act as routine dispose signal estimation route to, recursion least square method possess in a position to at approximate time bring relative comparison small error.
作为常规的处理信号估计的方法，递归最小二乘法具有能够在估计的时候产生相对比较小的误差。
Coefficient determination, absolute bias, relative absolute bias, root mean square error and relative root mean square error were employed to evaluate the precision of different model systems.
采用确定系数、绝对误差、相对绝对误差、均方根误差、相对均方根误差等模型评价指标对不同模型系统的精度进行比较分析。
Thirdly, the least-square method based on relative error is proposed and used to fit the calibration curve of the ultrasonic sensors.
第三，提出了“基于相对误差的最小二乘法”，并将其应用超声波传感器标定的数据拟合。
Thirdly, the least-square method based on relative error is proposed and used to fit the calibration curve of the ultrasonic sensors.
第三，提出了“基于相对误差的最小二乘法”，并将其应用超声波传感器标定的数据拟合。
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