Mining the Change of the Quality of Stored Grain

The quality of grain changes continually during its storage, including the change of its physical characteristics and physiological characteristics. This paper presents an approach to predict the change of the quality of stored grain with data mining technology. Logistic Regression, Decision Tree and Muitilayer Perceptron are applied to predict the change of the grains＇ quality control index and to obtain the grains＇ quality change probability. The grain sampling with higher probability can be processed earlier.     

缺失数据下局部M-估计的相合性和渐近正态性

在缺失响应变量的不完全数据下,对非参数回归模型进行研究,利用稳健的变窗宽局部线性回归的方法,给出了回归函数m(x)的估计,并证明了局部M-估计具有相合性和渐近正态性.所提出的方法继承了局部多项式回归的优点并且克服了最小二乘方法缺乏稳健性的缺点.并且使用变窗宽提高了所得M-估计的可塑性,使之能成功地处理空间非齐次曲线、异方差性及非均匀设计密度.所得估计的渐近结果为求渐近最优方案以及直接从数据估计最优变窗宽提供了理论基础.     

Response of downstream sediment to water resource development in mainstream of the Lancang River

The impact of hydropower cascades of the Lancang River on downstream sedimentation has become one of the key problems in the research of the transboundary water resource development and its possible environmental effects. In this paper, the newest sediment data series spanning 17 years （1987-2003） at Yunjinghong hydrometric station and Chiang Saen hydrometric station, in northern Thailand, are analyzed to reveal these effects. Monthly mean sediment concentration data of the two stations are compared in-phase and yearly series are analyzed by the method of correlation and regression analysis and causality test. The analysis result shows that, though both of them are located at lower reaches of Manwan and Dachaoshan hydropower stations which are already constructed and in use, the sediment responses of the two stations are different under the regulation of the hydropower stations Annual series at Yunjinghong station are positively correlated but do not match well with those at Chiang Saen station. In addition, the results of Granger causality test on the time series also suggest that the series of maximum monthly mean sediment concentration of the Yunjinghong station each year may give cause for the ones of Chiang Saen station, while the series of minimum monthly mean sediment concentration each year and annual mean sediment concentration of Yunjinghong do not evidently give cause for the ones of the other station. Undoubtedly, this discovery will provide new evidences to assess the impacts of cascades construction in mainstream of the Lancang River on the variation of downstream sediment and its transboundary effects.     

ZIP回归模型的局部影响诊断

基于EM算法和完全数据对数似然的条件期望，分别研究了ZIP回归模型在加权扰动、解释变量扰动和响应变量扰动下的局部影响诊断问题，得到了相应的诊断统计量．最后通过一个实例说明诊断统计量的有效性。     

烟尘水热化合反应制备钙基脱硫剂工艺优化

运用均匀试验设计和回归分析方法优化烟尘水热化合反应制备钙基脱硫剂工艺,得到比表面积回归方程．分析不同水热条件及其相互作用对钙基脱硫剂比表面积的影响,经优化筛选并兼顾实际操作得到最佳水热条件,制备的高效钙基脱硫剂比表面积超过120 m2/g,是单一Ca(OH)2的10倍以上,优化效果显著,可以为工业生产提供依据．     

利用调查数据中完全辅助信息的广义模型校正方法

推广了模型校正方法去估计总体和均值以外的总体参数.建议的模型校正估计可以处理任何线性或非线性的工作模型,且在线性模型的情形下变为广义回归估计.通过Kronecker积,可把模型校正方法用于估计双线性参数,特别地,去估计方差和协方差.     

回归函数的小波支持向量机鲁棒估计法

小波网络具有小波的多尺度特性和神经网络的自学习功能,在回归估计中得到广泛的应用,但其性能受到样本中粗差的严重影响.虽然以M-估计作为目标函数可以解决这个问题,但由于其对应的影响函数由残差绝对值决定,因此如何选择初始参数值成为一个关键问题.为此,提出回归函数的小波支持向量机鲁棒估计方法(小波支持向量回归,WSVR,Wavelet Support Vector Regression).该方法中首先提出并证明了一种新的小波支持向量机(WSVM,Wavelet Support Vector Machine),用于确定初始参数值方法,这种方法能够确定合理的网络结构和合适的初始参数值,保证含有粗差的样本点的残差绝对值较大;然后使用一种构造的M-估计作为目标函数,并提出了自适应确定阈值方法.仿真结果表明,使用这种方法得到的回归模型不仅具有良好的多尺度逼近特性,而且有较好的鲁棒性和较高的推广性能,具有较高的理论和应用价值.     

多气体传感器信号样条回归分析

回归分析研究某一变量和其他有关变量的依赖关系 ,它利用观测数据建立变量之间的回归模型 ,并做出预报和控制 .目前 ,回归分析已广泛应用于工程、经济、金融、生物和医学等领域 .将样条回归分析方法用于电子鼻系统中多气体传感器信号的信息处理 ,针对牛肉样本建立了牛肉新鲜度样条回归模型 ,并对该模型进行了回归分析 ,给出了牛肉新鲜度预报值和估计区间 .研究结果表明 ,样条回归分析适用于电子鼻系统在牛肉新鲜度检测中的信息处理     

生态系统数学模型的拟合程度

定义了回归方程的拟合程度.对非线性回归方程,要化为线性方程,用相关系数、显著性水平、残余标准差率来检查回归方程和拟合程度.