The forecast of the route travel time based on dynamic random time is stressed and studied in the mathematics model.
在构建道路网的数学模型中着重研究了基于动态随机时间的道路行程时间预测。
By simplifying the equation of air pollutant diffusing, the mathematic form of dynamic statistics model of air pollutant forecast has been worked out.
从空气污染扩散方程出发，经过简化推导，得出污染预报动力统计模型的数学形式。
RESULT:Markov model was a forecasting method of many merits, which can be used to forecast those economy dynamic states that were in line with the condition of hypothesis.
结果：马尔科夫模型是一种具有很多优点的预测方法，能够对符合假设条件下的各种经济动态进行准确预测。
Because many uncertain factors impact the internal resistance, this paper give a real time method and establish a dynamic innovation forecast model of GM (1, 1).
同时针对影响内阻变化的因素太多且不确定的情况，提出了实时在线的方法建立起动态新息的GM(1,1)预测模型。
Therefore, the dynamic gray forecast model had higher value than static model in dam deformation forecast.
因此，动态灰色预测模型在大坝变形的预测预报中比静态预测模型具有更高的应用价值。
The dynamic drawdown forecast was performed and the groundwater management model was developed, based on a numerical quasi-three-dimensional simulation model introduced.
引入准三维流数学模型进行地下水动态预报及建立地下水管理模型。
A gray dynamic model is present to forecast electricity forward price and results of different models are studied.
针对这一特点，用灰色动态预测模型对电力远期价格进行了预测，并对不同模型的预测结果进行了研究。
Using this model groundwater dynamic in 2010 is forecast.
用此模型预测了2010年的地下水动态。
Also, the model is established by the general office software and can be integrated with the current financial software to realize the real-time dynamic forecast for company financial risk.
同时该模型充分利用现有办公软件，易于和现有财务软件相结合，以实现对企业财务风险的动态实时预警。
Finally, the feasibility of model predictive control can forecast reasonable inventory level and control dynamic and uncertainty supply chain system are shown by the simulation results.
最后，仿真结果表明，模型预测控制技术在预测合理库存水平以及控制动态、不确定的供应链系统是可行的。
The forecast result proved that this model has many advantages of convenient use, fine dynamic function and high precision. The model has fine practical value.
通过预测结果的分析可看出，该模型具有利用方便、动态性能好、预测准确性高等优点，在实际中具有一定的实用价值。
The four-dimensional data assimilation is to integrate the current and past data into a forecast model equation for providing time continuity and dynamic coupling.
应用伴随方法求解以数值预报方程作为约束条件的四维变分资料同化方案 ，关键问题是如何构造伴随模式。
The four-dimensional data assimilation is to integrate the current and past data into a forecast model equation for providing time continuity and dynamic coupling.
应用伴随方法求解以数值预报方程作为约束条件的四维变分资料同化方案 ，关键问题是如何构造伴随模式。
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