Using GMS digital satellite cloud picture and conventional meteorological data in combination with forecasting experience,nowcasting application system of storm rainfall automatic quantitative has been established by means of intelligent network model,statistical regression,cloud index,et al.
In this paper, compared with statistics regression forecast method through the application. The result indicates that ANN is a kind of feasible,method equikal of examples to non-linear regression method, which has special superiority to the linar regression method.
Conjugated the practice of continuous casting of steel, the temperature change of 37Mn5 steel in ladle was numerically simulated by Finite Control Volume method and the temperature declination institution from ladle to tundish was analyzed and predicated by Statistics Regression and Neural Network method.
The statistics regression and design of experiment technologies are introduced as a methodology into the virtual design process. The selection principle of design of experiment and statistics regression model is systematically concluded.
Taking Yixing City of Jiangsu Province as the study area, the statistic regression models are established between LAI (Leaf Area Index) measured in field and the different forms of Vegetation Index (VI) derived from Landsat TM imagery data.
本文以江苏省宜兴市为研究区，利用现代卫星遥感技术，结合与其同步的实地调查采样数据和GPS测量技术，建立了从Landsat TM影像数据提取的植被指数(Vegetation Index，VI)与地面实测的叶面积指数(Leaf Area Index，LAI)的统计回归模型。
The deformation statistic regression analysis model has reflected the deformation rule of the dam in a standard way and provides effective analysis means and methods for evaluating the safety character of dam operation and predicting the development trend of dam deformation.
It adopts gradual regression analysis to establish the deformation statistic regression analysis model through analysis on observed deformation data of a gravity dam of a hydro-power station for years and considering the influence of water level, temperature and aging on dam deformation.
3. Because statistical regression methods are very difficult to obtain reliable results, regression variables introduced are extremely limited, and their comparative complex non-linear mapping are not been studied deeply, there are limitation for utilization of a massive of correlations information.
In the Statistical modeling phase, aimed at the phenomenon of serious relatively multicollinearity, by comparison to the diverse regression projects, the respective applications with Ridge regression and Partial Least Squares regression establishes SO_2 concentration forecasting equation, by compared with common statistical regression methods, the greatest advantages of Partial Least Squares regression method are its withdrawal to the biggest variables information components, elimination of harms from muticollinearity, and the most explanatory to variables.