mape

A Multicast Service Model Based on MAPE

基于MAPE结构的组播AAA模型

MAPE : A MIMD Architecture Performance Evaluating Simulation Environment

MAPE：一个并行系统结构性能评价模拟环境

Study on the Influence of MAPE Compatibilizer on Mechanical Properties of Wheat Straw / Polyethylene Composite Materials

MAPE相容剂对麦秸/聚乙烯复合材料力学性能影响的研究

Specific modeling steps and prediction accuracy measures ( RMSE , MAPE ) of the corrosion prediction model were given .

给出了腐蚀预测模型的具体建模步骤和预测精度评价指标（RMSE、MAPE）。

The construction of reactive power optimization based on MAS in a whole power system , the structure of sub-MAS system and MAPE are presented using agent .

利用AGENT技术提出了基于MAS（多主体系统）的全网无功优化系统结构、子MAS系统结构及MAPE（多主体处理环境）结构；

It needs three smooth parameters α、β、γ at the best , value want to carry on many combinations to experiment with make equally absolutely 100 cent error margin ( MAPE ) minimum .

它需要三个平滑参数，在确定最佳α、β、γ值时要进行多次组合试验，以使平均绝对百分误差（MAPE）最小。

It is used to calculate the prediction value of soil moisture and leaf water content . And then verified and modified by the really measured value and MAPE criteria of assessing and forecasting accuracy .

然后利用预测方程计算出土壤含水量、叶片含水量预测值，并利用采集的含水量实测值和MAPE评估预测准确度准则对预测方程进行检验和修正。

1.1 The dispersion of the clay in the exfoliated maleic grafted polyethylene ( MAPE ) / organo-montmorillonite ( OMT ) nanocomposites evolved under gamma-ray irradiation .

1考察了γ-射线辐照对马来酸酐接枝聚乙烯／有机蒙脱土（MAPE／OMT）层离型纳米复合材料结构与燃烧性能的影响。

The results showed the polyacrylate treatment could improve the dispersion of straw fiber in the interaction between straw fiber and the base material , and the introduction of MAPE could improve the interaction between straw fiber and the base material and the properties of the composite .

研究结果表明：采用聚丙烯酸酯处理剂可以有效地提高稻草纤维在树脂基体中的分散状况；在复合材料体系中引入MAPE可以提高稻草纤维和聚合物基体之间的相互作用，提高材料的综合性能。

The predict value whose error is less than 5.3 % is 98 . 3 % of total . Further more , the confidence level come to 0.981 . Compareed with quadratic loss function and Huber loss function , the MAPE is only 2 . 3 % .

预测显示误差小于5.3%的值占了总体的98.1%，其预测署信度达到0.983，与二次和Huber损失函数相比其MAPE值只有2.3%。