1) prediction of containers handling capacity
集装箱吞吐量预测
1.
The different relative factors such as function sets, terminator sets, possibilities of reproduction, crossover and mutation, termination precision were studied and tested, and the final model which is applied in the prediction of containers handling capacity was established and the algorithm was programmed.
在对现有各种预测方法进行简要回顾的基础上 ,对遗传规划方法进行了研究 ,提出了应用于集装箱吞吐量预测的计算模型和相关参数的确定方法 ,并完成了算法设计和编程工作 。
2) container throughput
集装箱吞吐量
1.
Application of combined model in forecasting container throughput capacity of a harbour;
组合模型在港口集装箱吞吐量预测中的应用
2.
Prediction of container throughput is the basis for making port development plan.
集装箱吞吐量预测是港口发展规划制定的依据。
3) port container throughput
港口集装箱吞吐量
1.
In view of the difficulties in identifying major factors for port containers throughput forecasting,based on the factors affecting the port container throughput,key factors were found by adopting the principal component analytical method.
针对港口集装箱吞吐量预测中重要指标难以选取的问题,从可能影响港口集装箱吞吐量的因素出发,采用主成分分析法,提取最关键影响因素。
4) throughput forecasting
吞吐量预测
1.
The grey system model is efficient for long-term port throughput forecasting.
灰色系统预测模型是一种进行港口吞吐量预测的有效方法。
2.
Applying the models for Shanghai throughput forecasting,the conclusion was educed.
将各模型用于上海港吞吐量预测,可得结论:串联模型和嵌入模型是利用灰色理论对多元回归模型的改进,其能弱化原始数据的随机型,提高模型预测精度;并联型模型本质上是组合模型,能综合多种信息,预测具有非劣性,具有一定的实用价值。
5) cargo throughput predicting
货物吞吐量预测
1.
The author s task in this thesis is to build RBF Network-Monte Carlo cargo throughput predicting model,predicting cargo throughput for Dalian port from 2006 to 2010,plus 2020 single year,based on historical data.
建立了RBF神经网络—Monte Carlo货物吞吐量预测模型,利用大连港货物吞吐量的历史数据,对2006-2010年以及2020年的大连港货物吞吐量进行预测。
补充资料：经验指数预测法(见发生量预测)
经验指数预测法(见发生量预测)
经验指数预测法见发生量预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条