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grey dynamic model group
相关语句
  灰色动态模型群
     Grey dynamic model group and its application in predicting of industrial water demand
     灰色动态模型群法及其工业取水量预测应用
短句来源
     Chapter 3 explores a forecast method for water quality. In view of the shortage of water quality information, this chapter puts forward the grey dynamic model group made up of several simple grey models to forecast the trend of river water quality, by combining the principle of the new superseding the old in grey system theory with the GM (1,1) model.
     第三章探讨了河流水质预测方法,针对水质实测资料较少、监测频率较低,且数据往往呈现一定波动性特征的事实,为提高中长期水质预测的精确性和可靠性,将灰色系统新陈代谢原理与GM(1,1)模型原理相结合,提出了水质预测的灰色动态模型群法。
短句来源
     Study result shows that, first, the grey dynamic model group can make full use of the recent information about water quality to predict the trend of water quality state;
     研究表明,灰色动态模型群法能够充分利用近期水质资料信息预测水质变化趋势;
短句来源
     GDMG (grey dynamic model group) can pay more attention to recent data and avoid using uncertain information to get more reasonable results.
     对于信息丰富、数据较多的原始数列,采用灰色动态模型群法进行预测,考虑近期信息对未来的影响,可避免单一灰色模型容易利用不稳定信息的缺陷,使预测精度更加准确。
短句来源
  灰色动态模型群法
     Grey dynamic model group and its application in predicting of industrial water demand
     灰色动态模型群法及其工业取水量预测应用
短句来源
     Chapter 3 explores a forecast method for water quality. In view of the shortage of water quality information, this chapter puts forward the grey dynamic model group made up of several simple grey models to forecast the trend of river water quality, by combining the principle of the new superseding the old in grey system theory with the GM (1,1) model.
     第三章探讨了河流水质预测方法,针对水质实测资料较少、监测频率较低,且数据往往呈现一定波动性特征的事实,为提高中长期水质预测的精确性和可靠性,将灰色系统新陈代谢原理与GM(1,1)模型原理相结合,提出了水质预测的灰色动态模型群法
短句来源
     Study result shows that, first, the grey dynamic model group can make full use of the recent information about water quality to predict the trend of water quality state;
     研究表明,灰色动态模型群法能够充分利用近期水质资料信息预测水质变化趋势;
短句来源
     GDMG (grey dynamic model group) can pay more attention to recent data and avoid using uncertain information to get more reasonable results.
     对于信息丰富、数据较多的原始数列,采用灰色动态模型群法进行预测,考虑近期信息对未来的影响,可避免单一灰色模型容易利用不稳定信息的缺陷,使预测精度更加准确。
短句来源
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On the basis of logarithmic transformation of orginal data, a grey dynamic model group is established made up of six simple grey models. Then the model group is used to predict the trend of concentration of NH3 - N in Huaihe River during dry season . Study result shows that, first, the grey dynamic model group can make full use of the recent information about water quality to predict the trend of water quality state; second, on the basis of reformation of orginal sequences,...

On the basis of logarithmic transformation of orginal data, a grey dynamic model group is established made up of six simple grey models. Then the model group is used to predict the trend of concentration of NH3 - N in Huaihe River during dry season . Study result shows that, first, the grey dynamic model group can make full use of the recent information about water quality to predict the trend of water quality state; second, on the basis of reformation of orginal sequences, this method can improve the imitation effect of random data sequences and heighten the precision of prediction result.

在对原始数据序列对数变换的基础上,依据灰色系统理论,构造了由6个GM(1,1)模型组成的灰色动态模型群,并用于淮河干流枯水期氨氮浓度变化趋势预测。研究表明,灰色动态模型群法能够充分利用近期水质资料信息预测水质变化趋势;相对单个GM(1,1)模型,灰色动态模型群法能有效改善随机波动数据序列的拟合效果,提高预测精度。

GDMG (grey dynamic model group) can pay more attention to recent data and avoid using uncertain information to get more reasonable results. The method of GDMG is introduced in this paper. A middle and long term forecast for industrial water demand of Ji'nan city is made by GDMG. The predicted results of GMDG are reliable and practical by comparing with actual data and other method results.

对于信息丰富、数据较多的原始数列,采用灰色动态模型群法进行预测,考虑近期信息对未来的影响,可避免单一灰色模型容易利用不稳定信息的缺陷,使预测精度更加准确。本文介绍灰色动态模型群法的建模原理,并利用该方法对济南市工业取水量进行了中长期预测。通过与实际值及其它方法对比表明,该方法预测结果可靠,受人为因素影响较小,实用方便。

 
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