进行预测

基本解释 (translations)

pretest  ·  pretested  ·  pretests

与进行预测相关的网络例句 [注：此内容来源于网络，仅供参考]

The structure and its function for forecast are discussed in detail after summing up the prediction of the South Yellow Sea M6.1 earthquake on Nov. 9, 1996.In the meanwhile,the trend of strong earthquake is researched by commensurability modeling.

在回顾总结1996年11月9日南黄海6.1级地震预测的基础上，对南黄海海域1846年以来6级强震可公度性的基本结构及其预测功能进行了深入研究，同时建立了可公度预测模型，对该区未来强震趋势进行预测探讨。

Model 2:Model 1 can predicts various indicators of the regional population, but the parameters trends to uncertainty during the long-term, and the cumulative effect of error is significant.

模型一能得到关于各区域人口的各项指标，但在对人口长期趋势进行预测时，模型一中各参数存在较大的不确定性、且误差的累积效应明显，在进行长期预测时达到人口高峰后一直下降，不再有波动，这与实际历史上的三次高峰不符合，故在模型二中挖掘数据内在的相关信息，利用2001-2005年中国国家统计年鉴数据及模型一中短期预测的人口总数建立灰GM(1，1)模型对我国人口总数进行长期预测。

Among used machine learning methods, the gradient descent method is widely used to train various classifiers, such as Back-propagation neural network and linear text classifier. However, the gradient descent method is easily trapped into a local minimum and slowly converges. Thus, this study presents a gradient forecasting search method based on prediction methods to enhance the performance of the gradient descent method in order to develop a more efficient and precise machine learning method for Web mining.However, a prediction method with few sample data items and precise forecasting ability is a key issue to the gradient forecasting search method. Applying statistic-based prediction methods to implement GFSM is unsuitable because they require a large number of data items to model a prediction model. In the contrast with statistic-based prediction methods, GM(1,1) grey prediction model does not need a large number of data items to build a prediction model, and it has low computational load. However, the original GM(1,1) grey prediction model uses a mathematical hypothesis and approximation to transform a continuous differential equation into a discrete difference equation in order to model a forecasting model.

其中梯度法是一个最常被使用来实现机器学习的方法之一，然而梯度法具有学习速度慢以及容易陷入局部最佳解的缺点，因此，本研究提出一个梯度预测搜寻法则(gradient forecasting search method， GFSM)来改善传统梯度法的缺点，用来提升一些以梯度学习法则为基础的分类器在资讯探勘上的效率与正确性；而一个所需资料量少、计算复杂度低且精确的预测模型是梯度预测搜寻法能否有效进行最佳解搜寻之关键因素，传统统计为基础之预测方法的缺点是需要较大量的数据进行预测，因此计算复杂度高，灰色预测模型具有建模资料少且计算复杂度低等优点，然而灰色预测理论以连续之微分方程式为基础，并且透过一些数学上的假设与近似，将连续之微分方程式转换成离散之差分方程式来对离散型资料进行建模及预测，这样的作法不尽合理，且缺乏数学理论上的完备性，因为在转换过程中已经造成建模上的误差，且建模过程仅考虑相邻的两个资料点关系，无法正确反应数列未来的变化趋势。

The general orthogonal polynomial is adopted for model approximation of DPS and the predictive control of DPS is translated into the lumped one. Model algorithmic control is applied to deal with the lumped parameter system and a control law is given. The predictive controller of DPS is obtained by inverse transformation. The method and steps are discussed in detail.

该方法采用广义正交多项式对分布参数系统的模型进行逼近，将离散时间分布参数系统的预测控制问题转化为集中参数系统的预测控制问题，运用模型算法控制方法对集中参数系统进行预测控制，求出控制律，经反演变换得出分布参数系统的预测控制。

The phase space reconstruction theory of chaotic dynamic system, in combination of the non-linear reflecting and pan-capacity of neural network, can be used to establish the prediction model; and a kind of new prediction method is suggested to realize the complete tracing of phase point evolution process and to predict "the price nails", whereby improving the prediction accurateness and effectively solving the problem of negative prediction, with the satisfactory results obtained.

采用混沌理论预测系统边际电价针对我国电力市场电价变化特点，利用电价和负荷时间序列的混沌特性，重构准确的电价序列相空间，通过跟踪相空间相邻相点的演化趋势，建立基于快速BP网络的电价预测模型，对我国川渝电网电价进行预测，取得良好效果利用混沌动力系统的相空间重构理论，结合神经网络的非线性映射和泛化能力建立预测模型，提出一种新的预测方法实现了相点演化过程的全局跟踪，对"价格钉"进行预测，提高了预测精度，有效解决了负预测问题，得到满意的结果。

First the theory of grey system and of principle grey prediction model is presented.Based on the data of sown areas of farm crops in Hubei Province for 9 years,the GM(1,1) grey prediction model is established and the precision of prediction is higher by checking the result,so the model may be applied to the prediction of sown areas of farm crops in Hubei Province for futural severe years.

中文摘要：首先介绍了灰色系统理论及灰色预测建模原理，以湖北省连续9年的农作物播种面积数据为基础，建立GM(1，1)灰色预测模型，对湖北省未来8年的农作物播种面积进行了预测，并对预测结果进行检验，预测结果精度较高，可以用来对湖北省未来几年的农作物播种面积进行预测。

And, it establishes the computer model with the Scott·Henseleit method, which analyses and optimizes the ventilation network and auto-creates the figures of the evading disaster and succoring disaster with the computer. The paper establishes the GM(1,1) gray forecast model, and forecasts the amount of gasemission of the laneway based on the history data, and then compartmentalizes the danger grade for the laneway. Utilizing the principle and method of cybernetics, the paper brings the feed-forward and feedback. controlling method on the hidden risk in the mine, namely, the double-loop model of forecasting and early- warning. Especially in the feed-forward forecasting, the paper adopts the one unit non-linear regression analysis and forecast method, and forecasts the hidden risk amount in the mine during some time, and compartmentalizes the hidden risk grade based on the history data.

论文中，利用斯考特·亨斯雷法建立计算机模型，对矿井通风系统进行解算和优化调节，并通过计算机自动生成避灾、救灾路线图；建立灰色GM(1，1)预测模型，对巷道瓦斯涌出量进行预测，进而对巷道中的瓦斯事故危险性进行危险等级划分；利用控制论的原理和方法，提出了煤矿通防隐患前馈——反馈控制方法，即预测、预警双环模型，采用一元非线性回归分析预测方法，对煤矿某一段时间内的隐患数量进行预测，并结合历史数据，对当前煤矿隐患排查处理状况划分等级，分别予以管理和控制。

In this paper, different coal face 3801 in a mine gas emission time is based on gray theory of gas emission prediction method, a mine gas emission in GM (1, 1) prediction model made its prediction of gas emission, by making the residual test, the results show that the prediction accuracy can be predicted.

本文以某矿3801采煤工作面不同时间段瓦斯涌出量为依据，运用灰色理论提出了瓦斯涌出量的预测方法，建立了矿井瓦斯涌出量的GM(1 ，1)预测模型，并对其进行了瓦斯涌出量的预测，通过进行残差检验，结果表明预测精度高，可以进行预测。

The research of this paper includes three parts: the first, according to the statistical figures about the cargo transport in recent years, it analyzes cargo transport structure and the changing law and the internal reason of cargo transport. At the same time it analyzes and calculates the change of the arriving ships" structure for a systematic and full understand of the transport demands. The second, it make a scientific forecast of the port"s future cargo capacity making use of the grey forecast system, which provides scientific basis for medium-long term development plan of the ports cargo handling capacity. It also makes some analysis and research for medium-long term development future of Zhangjiagang port in use of physical unit analising technique, looks for the scattered no-order and no-system reason of medium-long term development. On the basis of summarizing their experience, the article adopts physical unit model to make a comprehensive judgement. It has distinctive opinion of value in accord with scientific principles for medium-long term development of Zhangjiagang port.

本文研究的工作主要分三个部分：第一，根据张家港近年来有关货物运输的一些统计数据，对货物运输的结构和规模演变的规律及内在原因作一剖析，同时对到港船舶的结构变化进行预测，以求对运输需求有一个系统全面的了解；第二，应用灰色预测系统对张家港港未来港口货运吞吐量进行科学预测，为张家港港货物装卸能力规划提供科学依据；应用物元分析技术对张家港港口的未来发展前景作出一定程度的分析、研究，找出张家港港口未来发展规划的零散无序、不成系统的因素，总结前人经验的基础上采用综合评判物元模型进行评判，在预测港口未来发展规划是否科学实际上具有独到见解。

The research work of this article is made up of theory and application, it includes three partsrthe first, it takes SWOT analytical technology to analyse Zhangjiagang port" s developing strategic enviorment . The second, it according to the statistical figures about the cargo transport in recent years, it analyzes cargo transport structure and the changing law and the internal reason of cargo transport. At the same time it analyzes and calculates the change of the arriving ships" structure for a systematic and full understand of the transport demands. The third, it make a scientific forecast of the port" s future cargo capacity making use of the grey forecast system, which provides scientific basis for medium-long term development plan of the port"s cargo handling capacity. It also makes some analysis and research for medium-long term development future of Zhangjiagang port in use of physical unit analising technique, looks for the scaltered no-order and no-system reason of medium-long term development.

本文从理论和应用两个部分对港口发展战略进行了研究，研究的工作主要分三个部分：第一，应用SWOT法对港口发展环境进行技术分析；第二，在对国内外同类型港口进行分析的基础上，对张家港港的发展做一比较；根据张家港近年来有关货物运输的一些统计数据，对货物运输的结构和规模演变的规律及内在原因作一剖析，同时对到港船舶的结构变化进行预测，以求对运输需求有一个系统全面的了解；第三，应用灰色预测系统及三次指数平滑法和组合预测法对张家港港未来港口货运吞吐量进行科学预测，为张家港港货物装卸能力规划提供科学依据。

与进行预测相关的网络解释 [注：此内容来源于网络，仅供参考]

Cash Flow Forecast )：現金流預測

部分短期预测以现金流预测(cash flow forecast)的形式呈现,亦即对未来几个月或几个季度内企业现金的流出与流入进行预测. 其中所记录的现金收入和支出,是以预期的销售收入和各种应付的成本与费用为基准的. 公司的销售预测指根据某一特定期间内的预计销售额进行评估.

COL：行

对基于家的学校出行(HB S)和由集体户居民完成的出行(COL)而言,无需对出行吸引率进行预测,因为此类出行分布可通过一个非常规的约束分布模型来完成. 在该种方法中,出行发生量将根据出行长度进行分布,

market forecast：市场预测

市场预测(Market Forecast)是一门掌握市场动态变化的科学,是在市场调查和市场分析的基础上,运用科学的方法对市场需求和企业需求以及影响市场需求变化的诸因素进行分析研究,通过已发生的、呈现出来的大量信息,对未来的发展趋势作出判断和推测,

Imp：互调产物

本文介绍了一种采用微软Excel V5.0编制的对船体产生的互调产物(IMP)进行预测计算的模型. 该计算模型的频率和模式信息来自舰船通信计划和最近测试到的船体产生的IMP情况,并且可以预测IMI受害接收机频率和相应的高频互调干扰源发射频率.

predication：預測

⑷预测(predication) 预测是利用历史数据找出变化规律,建立模型,并由此模型对未来数据的种类及特征进行预测. 预测关心的是精度和不确定性,通常用预测方差来度量. ⑸时序模式(time-series pattern) 时序模式是指通过时间序列搜索出的重复发生概率较高的模式.

Predictive Coding：预测编码

在图像数据压缩技术中,变换编码(Transform Coding)与预测编码(Predictive Coding)已成为最基本的两种编码方法. 变换编码的基本思想是,将空间域描写的图像信号变换到一个正交变换域(正交的矢量空间)进行描写. 变换前后的明显差别是,

sensitive：敏感性

在介绍算法之前,我们先介绍一下衡量一个算法优劣的标准:敏感性(sensitive)和特异性(specificity). 假设待测序列中有M1条序列是基因序列,剩余的M2条为非基因序列. 我们用程序对待测序列进行预测,N条序列被预测为基因,其中有N1条确实为基因,

random shocks：随机冲击

在它们的模型中,"随机冲击"(random shocks)常常会使资产价格低于预测值. 事实上,地震、干旱或其它外力都不会导致价格下跌. 根本原因在于使用了严重错误的模型来进行预测. 投机者和购房者认为租金或建筑成本会上升,于是押注未来的房价会上涨,这同时也抬高了现有房

Unpredictability：不可預測性

有效市场假设的第二个原则是"不可预测性"(unpredictability). 在一个有效的市场中,基于现存的公开信息对股票未来的价格进行预测是不可能的. 早期,很多对这个原则的背离的现象都跟行为没有明确的联系. 有报道表明,小公司,

Making predictions：预测

前两期,我们对进行预测(Making predictions)和谈论假想的情景(Talking about imaginary situations)这两个话题在英语中的常见表达法进行了学习. 本期将对其考点与热点问题进行专题练习. 请做下面的单项填空题,并阅读题后的参考答案与简析.