本文采用一种累加模型将复杂大规模网络流量分解成趋势项、周期项和随机项。
In this paper, according to the characteristics of non linear network traffic, traffic behaviors are decomposed into trend items, period items, and random items by an accumulation decomposition model.
利用随机过程的方法来分析系统误差的变化过程，建立了系统误差的随机过程模型——误差累加模型和维纳过程模型。
Rules of the system error along with time is represented and stochastic process models are established: error accumulating model and Wiener model.
实例表明，一次累加法预测模型精度高，预测结果可靠，可用于城市燃气年负荷预测。
The practical example shows that the forecast model based on once accumulation method has high precision and reliable result, it can be used to forecast the annual city gas load.
用位移算符方法计算量子噪声的累加，同时建立了有衰减和噪声时的信道的量子模型。
With displacement operator method the total quantum noise is obtained, and a quantum channel model with attenuation and noise is proposed.
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。
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.
应用灰色系统理论，由油井重复压裂前的递减产量，生成相应的一阶累加序列，由累加序列建立了灰色预测模型GM (1,1)。
A grey predicting model of GM (1, 1) can be established with the accumulated sequence generated by the oil well decline rate before refracturing according to the theory of grey system.
为了解决多层的少样本或无规则数据的建模问题，在一般多层统计模型的基础上提出了多变量整体模式的累加多层统计模型。
For modeling the multilevel few sample or irregular data, the accumulated multilevel statistical models of multivariate full model was built on ordinary multilevel statistical models.
简单累加信用度计算模型和均值信用度计算模型是以信任模型为核心的在线信誉管理系统中两种主要的信用度计算模型。
Simple adding credit calculation model and average credit calculation model are the two main credit calculation models in the online reputation management systems whose core is the credit 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。
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.
将GM(1,1)模型与随机数据的累加生成相结合，对未来异常降水发生时刻作预测。
It is shown that by combining GM (1, 1) with the addition generation of random Numbers, can be employed to predicts the time of forthcoming abnormal seasonal precipitation.
若要准备好面对行为是模型第一个不会，根据预设，执行累加式的结构描述更新到您的资料库。
A behavior to be prepared for is that Model First does not, by default, perform incremental schema updates to your database.
若要准备好面对行为是模型第一个不会，根据预设，执行累加式的结构描述更新到您的资料库。
A behavior to be prepared for is that Model First does not, by default, perform incremental schema updates to your database.
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