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最小二乘配置与REHSM求解GPS应变场的方法

首先在最小二乘配置方法中,通过多项式函数、高斯函数、似高斯函数、希尔沃宁函数、空间调和函数(2个)共6种协方差拟合函数,在WGS-84椭球面上比较获得最优的区域速度场信息。然后在研究区域进行划分与编码,通过块体的整体旋转及均匀应变模型(REHSM)解算得到应变场。最后对运城盆地2007~2010年19个基于"数字地震网络工程"的监测数据进行分析,给出该区域的应变场分布。     

基于灰色最小二乘支持向量机的大坝变形预测

提出一种基于灰色最小二乘支持向量机的大坝变形预测新算法。通过对原始大坝序列进行一次累加,弱化序列中随机扰动的影响,增强数据的规律性,进而建立最小二乘支持向量机预测模型,并采用网格搜索法选取最优参数。算法充分利用了最小二乘支持向量机泛化能力强、非线性拟合性好等优良特性,避免了灰色方法及模型存在的理论缺陷。与灰色GM(1,1)和单一最小二乘支持向量机对比表明,新算法能保证较优的局部预测值和较好的全局预测精度,应用于短期大坝变形预测是可行的。     

系统辨识在水位流量关系中的应用

用传统方法拟合平稳河流的水位流量关系,往往精度不高,与实际测量不能很好的重合.对于局部线性的水位流量关系,用系统辨识理论对其进行建模,利用最小二乘法,拟合精度明显提高.     

最小二乘法的改进算法辨识棱镜摄谱仪定标多项式

提出了采用最小二乘法的改进算法拟和一元非线性函数，并用该算法辨识棱镜摄谱仪的定标多项式-数值计算结果表明：采用改进的算法，非常显著地降低了求解定标多项式系数方程组的系数矩阵的条件数，定标多项式能更精确地反映谱平面坐标和谱线波长的定量关系。     

Determining Rayleigh damping parameters of soils for finite element analysis

Soil damping ratio is an important parameter for modelling the dynamic behaviour of soil embankment structures, especially for the micro‐vibration analysis of hospitals and high‐tech industries, which have a very severe construction requirement for vibration. This paper explores using a least‐squares method to evaluate the Rayleigh damping parameters for the finite element analysis of wave propagation in soils. The least‐squares method was first used in Bornitz's equation to calculate the absorption coefficients of soils. Afterward, the best‐fit Rayleigh damping parameters were obtained using those coefficients in the least‐squares scheme again. Both three‐dimensional finite element analyses and field experiments were performed to validate the accuracy of this method. The comparison between experimental and finite element results demonstrates that the accuracy of this least‐squares method is acceptable. Copyright © 2007 John Wiley & Sons, Ltd.     

反演奇异模型的参数估计的通用公式

将大地测量反演问题中的线性模型，综合为一般模型，称为反演奇异模型。运用最小二乘统一理论给出了反演奇异模型的参数和单位权方差通用估计公式，给出了这种模型的验前验后方差一致性检验方法。讨论了反演参数估计的有关问题。     

阻尼最小二乘法在射线追踪中的应用

利用弯曲法进行有倾角的层状介质的两点射线追踪，并采用改进的阻尼最小二乘法迭代求解。为了减小迭代初值的影响，采用分段线性插值调整迭代初值，使这种方法具有较好和较快的收敛性，对不同层数和倾角的层状介质均适用。     

THE PERFORMANCE OF LEAST SQUARES AND ROBUST REGRESSION IN THE CALIBRATION OF ANALYTICAL METHODS UNDER NON-NORMAL NOISE DISTRIBUTIONS

By means of Monte Carlo simulations a comparison has been made between ordinary least squaresregression and robust regression. The robust regression procedure is based on the Huber estimate and iscomputed by means of the iteratively reweighted least squares algorithm. The performance of bothprocedures has been evaluated for estimation of the parameters of a calibration function and fordetermination of the concentration of unknown samples. The influence of the distributionalcharacteristics skewness and kurtosis has been studied, and the number of measurements used forconstructing the calibration curve has also been taken into account, Under certain conditions robustregression offers an advantage over least squares regression.