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1)  predictive modeling
预测建模
1.
Predictive Modeling Method of Gini Coefficient and Its Application in Beijing Economic Development Analysis;
基尼系数的预测建模方法及在北京市经济发展分析中的应用
2.
Traditional approach to predictive modeling of large-scale sequential curves is to build model separately according to every curve,which causes heavy and complicated modeling work inevitably.
传统方法解决大规模时序曲线的预测建模问题,需要对每条曲线逐一建模,使得建模工作量相当庞大,在实际应用中缺乏可操作性。
2)  forecast modeling
预测建模
1.
Based on the historical data,a forecast modeling method for structural equation model was discussed,where the future relationship between the system factors was described without future sample.
提出一种结构方程模型的动态预测建模方法,从而可以在无须未来样本数据的情况下,预测系统要素之间未来的因果关系。
2.
Aiming at a hard problem to crack for the forecast modeling for flight accident rates,the forecast model of flight accident rates is established by using of the LS-SVM method based on statistical learning theory.
针对飞行事故率预测建模难的问题,基于统计学习理论,采用LS-SVM方法,建立了飞行事故率的预测模型。
3)  forecast model
预测建模
1.
A new machine learning method—support vector machine (SVM) is used tobuild forecast models on the nonstationary time series in this paper, and theapplication in weather prediction field by this method is tested and analyzed aswell.
全文分为以下三个部分: 在论文的第一部分中,我们系统地介绍了支持向量机方法的基本思想、特点、回归方法的内容、预测建模思路以及支持向量机学习建模软件平台等。
4)  model and forecast
建模和预测
5)  modeling and forecasting
建模与预测
1.
An RBF neural network based method for stocks market modeling and forecasting is presented,and a hierarchical genetic algorithm is proposed to train network parameters such as RBF centers,widths,connection weights and the configuration.
对上证综指和个股 (伊利股份 )的建模与预测结果表明 ,该训练方法使 RBF神经网络具有很强的学习与泛化能力 ,它在股市这样一个复杂的非线性随机系统建模中具有很高应用价
2.
This paper presents a method for stock market modeling and forecasting based on fuzzy neural network, genetic algorithm is used to learn the connection weights of the fuzzy neural network and partitions of fuzzy subsets.
提出一种基于模糊神经网络的股票市场建模与预测方法,并采用遗传算法训练网络权值及模糊子集的划分。
6)  forecasting modeling
建模的预测
补充资料:发育进度预测法(见发生期预测)


发育进度预测法(见发生期预测)


  发育进度预测法见发生期预测。
  
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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