Chinese translation for "平均绝对误差"

average absoulute error
mean absolute error
mean absolute value of error mae
mean atsolute deviation

Related Translations:

绝对误差:  a olute erroraa oluteerrorabs eabs. eabsolute uncertaintyabsolute value errorabsoluter fehler absolute erroradzae absolute errorerror absolute

绝对误差 绝对误差:  absoluteerrorerrorabsolute

参数绝对误差:  absolute divergence of parameter

最大绝对误差:  maximum absolute error

最小绝对误差:  lae least absolute errorleast absolute error

绝对误差积分:  integral of absolute value of error

绝对误差积分准则:  integral of absolute value of error criterion

时间加权绝对误差:  time-weighted absolute error

Example Sentences:

	1.	The most important criteria that used to check the calibrated model are root mean square error ( rms ) , the mean absolute error normalized rms error , and mass balance 模型参数使用试错法识别，识别过程中最重要的指标是均方差、平均绝对误差、标准均方差和水均衡。
	2.	Results show that the rbfnn is obviously superior to the traditional linear model , and its mae ( mean absolute error ) and rmse ( root mean square error ) are 41 . 8 and 55 . 7 , respectively 结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差( mae )和均方误差( rmse )达到41 . 8和55 . 7 。
	3.	Temperature predicting ann model for the next 24 hours is established . by this method , mean absolute error ( mae ) is reduced to 0 . 4512 ? from 0 . 6663 ? that is calculated by improved ashrae calculation method 建立了温度24小时提前预测的人工神经网络模型，使得24小时提前逐时温度预测平均绝对误差从改进ashrae计算方法的0 . 6663降低到了0 . 4512 ，平均相对误差从2 . 02降低到了1 . 36 。
	4.	Mae of hourly load prediction reduced to 65 . 07kwh and eep reduced to 2 . 60 % . this kind of model has not been reported by literature . a cost - minimum model for ice storage system is established and numerical calculation is carried out 建立了空调逐时负荷的24小时提前预测多点输出动态模型，更进一步提高了负荷预测的精度，使得逐时负荷预测平均绝对误差降低到了65 . 07kwh ，期望相对误差降低到了2 . 60 。
	5.	The calibration of flow model is acceptable with average rms of 0 . 7m , residual mean of - 0 . 045 m , average absolute mean error of 0 . 1 m and normalized rms value of 2 . 3 % . the contour map of the simulated heads , elaborated acceptable model calibration compared to observed heads map 模型结果中，均方差为0 . 7m ，平均误差为- 0 . 045m ，平均绝对误差为0 . 1m ，标准均方差为2 . 3 ，模拟地下水流场与实际观测地下水流场基本一致，说明所建立的数值模型符合该地区的实际水文地质条件。
	6.	During the course of the research , the criterions of the interpolation effect are mean error ( me ) , mean absolute error ( mae ) , root mean squared interpolation error ( rmse ) and the difference of mean square deviation between the measured and the estimated surface air temperature . the conclusions are as follows : ( 1 ) by contrasting the gaussian weighted model associated with the error modification with the gaussian weighted model , the error modification is proved to considerably ameliorate the precision of spatial interpolation ; ( 2 ) on the base of the gaussian weighted model , taking altitudinal effect into account can reflect the trend in which temperature changes according to the topographic altitude and may ameliorate the precision of spatial interpolation correspondingly and apparently , which indicates that topographical effect on the preciseness of spatial interpolation can not be disregarded in terms of the region with complicated topography ; ( 3 ) the map of daily surface air temperature distribution , using the modified gaussian weighted model a and b , can accurately reflect the temperature - changing - with - topographical - altitude trend . among them , the better is the model a , whose me is below 0 . 03 ? 在此过程中，采用平均误差( me ) ，平均绝对误差( mae ) ，插值平均误差平方的平方根( rootmeansquaredinterpolationerror ，简称rmsie ) ，插值前后测站要素值的均方差( meansquaredeviation ,简称msd )差值作为判定插值效果的标准，得出如下结论：通过高斯权重法与结合逐步订正的高斯权重法的对比，说明结合逐步订正方案的高斯权重法可大大提高地面日气温的插值精度；在高斯权重法中加入海拔影响项可以反映出温度随地形高度的变化趋势，同时也能较大地提高地面日气温的空间插值精度，说明在地形复杂的区域，地形影响在插值精度中是不可忽略的；对于高斯权重法的两种改进方案得到的地面日气温分布图都能很好地反映出表面大气气温随地形高度的变化趋势。
	7.	According to the research results from som model , 8 sub neural network is adopted in inner and mae of hourly cooling load prediction is reduced 80 . 64kwh . expected error percentage ( eep ) is reduced to 3 . 27 % . next 24 hours hourly cooling load prediction multi - output dynamic model is established and prediction accuracy is improved again 建立了一个统一的空调逐时负荷的24小时提前人工神经网络预测模型，并根据对日冷负荷类型的som分类结果，通过在内部一共采用8个子神经网络模型使得逐时负荷预测平均绝对误差降低到了80 . 64kwh ，期望相对误差降低到了3 . 27 。

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