1) weighting accumulated generating operation
加权累加生成
1.
As the traditional GM(1,1) model does not reflect the importance of the new information,this paper presented the concept of weighting accumulated generating operation and made a research on its properties including monotonic property,grey exponent law and convexity.
针对这一问题提出了加权累加生成的概念,并对加权累加生成在单调性、灰指数规律、凸性等方面的性质进行了研究,得到加权累加生成序列具有单调递增性,具有较强的指数规律,并具有下凸性,然后建立了基于加权累加生成的GM(1,1)模型。
2) accumulated generating
累加生成
1.
Methods: Based on the theory of accumulated generating opposite-direction GOM (1,1) of the set-up model, the grey pharmacokinetics model of fleetness intravenous infusion was made.
方法 :根据反向累加生成GOM(1,1)的建模原理 ,给出快速静脉推注药物动力学的灰色模型。
2.
A definition of accumulated generating operation in opposite direction is posed in this paper corresponding to traditional accumulated generating operation, gives a grey model GOM(1,1), provides a new generating method to grey modeling.
相对于传统的累加生成提出了反向累加生成的定义 ,给出了灰色 GOM(1,1)模型 ,为灰色建模提供了新的生成方
3.
The Accumulated Generating Operati on in reciprocal number has been given in this paper corresponding to traditional accumulated generating operation, given the grey model GRM(1,1) and Its Appl ication on Pharmacokinetic, provided new generating method grey model.
相对传统的累加生成提出倒数累加生成的定义 ,并给出其灰色 GRM( 1,1)模型及其在药物动力学中的应用 。
3) accumulation generation
累加生成
1.
It takes the actual measurement noise of container ship A s superstructure cabins as the training sample which is treated by accumulation generation in the grey forecasting method.
以A集装箱船上层建筑舱室噪声实测值为训练样本,利用灰色预测中"累加生成"的优点对训练样本进行处理,使其更具规律性。
4) Mixed Generalized Accumulated Generating Operation(MGAGO)
混广义累加生成
1.
This paper presents a new GM(1,1) forecasting model with Mixed Generalized Accumulated Generating Operation(MGAGO) based on the traditional GM(1,1) model.
在传统累加的GM(1,1)模型基础上,提出了一种新的基于混广义累加生成的GM(1,1)预测模型。
5) Method of muti accumulating generation
多次累加生成法
6) accumulated generating operation
累加生成操作
1.
Second, grey accumulated generating operation (AGO), a basis of the grey theory is effective to reduce randomness.
针对神经网络建模预测时,其建模精度往往受到数据随机性的影响,以及灰色累加生成操作(AGO)具有减小数据随机性,使数据变得有规则的特点,提出了一种新型的建模预测模型———灰色径向基(RBF)神经网络模型。
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