为满足电力系统实时动态观测的要求,需要精确估计电网络参数,基于电力系统的任何网络参数误差都会影响状态估计中的量测残差,通过对量测残差的分析提出了一个基于最小二乘的电网络参数误差估计方法。分两步完成电网络参数误差的估计:首...为满足电力系统实时动态观测的要求,需要精确估计电网络参数,基于电力系统的任何网络参数误差都会影响状态估计中的量测残差,通过对量测残差的分析提出了一个基于最小二乘的电网络参数误差估计方法。分两步完成电网络参数误差的估计:首先根据动态系统的量测方程,利用加权最小二乘法(weighted least squares method,以下简称WLS),同时引入网络节点虚拟量测量作为等式约束条件建立最优化模型,利用拉格朗日乘法提出量测残差的表达式,网络节点虚拟量测量的引入加强了算法的精确性和收敛速度;第二步首先利用递推最小二乘建立量测残差方程中偏差项的的迭代公式,电网络参数误差包含在量测残差方程的偏差项中,通过迭代得出一个无偏估计的初始状态量,最后求得电网络参数误差。IEEE9节点系统的仿真验证了算法的正确性和有效性。展开更多
This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presen...This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presented.Finally,a simulated adjustment problem is constructed to explain the method given in this paper.The results from the semiparametric model and G_M model are compared.The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method.展开更多
In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the...In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the assumption that the distribution of observations is unimodal and symmetry, this method can give the estimates of the parametric. Finally, two simulated adjustment problem are constructed to explain this method. The new method presented in this paper shows an effective way of solving the problem; the estimated values are nearer to their theoretical ones than those by least squares adjustment approach.展开更多
Feature initialization is an important issue in the monocular simultaneous locahzation ana mapping （SLAM） literature as the feature depth can not be obtained at one observation. In this paper, we present a new featu...Feature initialization is an important issue in the monocular simultaneous locahzation ana mapping （SLAM） literature as the feature depth can not be obtained at one observation. In this paper, we present a new feature initialization method named modified homogeneous parameterization （MHP）, which allows undelayed initialization with scale invariant representation of point features located at various depths. The linearization error of the measurement equation is quantified using a depth estimation model and the feature initialization process is described. In order to verify the performance of the proposed method, the simulation is carried out. Results show that with the proposed method, the SLAM algorithm can achieve better consistency as compared with the existing inverse depth parameterization （IDP） method.展开更多
This paper studies the parameter estimation of one dimensional linear errors-in-variables(EV) models in the case that replicated observations are available in some experimental points.Asymptotic normality is establis...This paper studies the parameter estimation of one dimensional linear errors-in-variables(EV) models in the case that replicated observations are available in some experimental points.Asymptotic normality is established under mild conditions, and the parameters entering the asymptotic variance are consistently estimated to render the result useable in construction of large-sample confidence regions.展开更多
We study the properties of the Lasso in the high-dimensional partially linear model where the number of variables in the linear part can be greater than the sample size.We use truncated series expansion based on polyn...We study the properties of the Lasso in the high-dimensional partially linear model where the number of variables in the linear part can be greater than the sample size.We use truncated series expansion based on polynomial splines to approximate the nonparametric component in this model.Under a sparsity assumption on the regression coefficients of the linear component and some regularity conditions,we derive the oracle inequalities for the prediction risk and the estimation error.We also provide sufficient conditions under which the Lasso estimator is selection consistent for the variables in the linear part of the model.In addition,we derive the rate of convergence of the estimator of the nonparametric function.We conduct simulation studies to evaluate the finite sample performance of variable selection and nonparametric function estimation.展开更多
By analyzing the structure of the objective function based on error sum of squares and the information provided by the objective function, the essential problems in the current parameter estimation methods are summari...By analyzing the structure of the objective function based on error sum of squares and the information provided by the objective function, the essential problems in the current parameter estimation methods are summarized: (1) the information extracted from the objective function based on error sum of squares is unreasonable or even wrong for parameter estimation; and (2) the surface of the objective function based on error sum of squares is more complex than that of the parameter function, which indicates that the optimal parameter values should be searched on the surface of the parameter function instead of the objective function. This paper proposes the concept of sample intersection and demonstrates the uniqueness theorem of intersection point (namely the uniqueness of optimal parameter values). According to the characteristics of parameter function surface and Taylor series expansion, a parameter estimation method based on the sample intersection information extracted from parameter function surface (PFS method) was constructed. The results of theoretical analysis and practical application show that the proposed PFS method can avoid the problems in the current automatic parameter calibration, and has fast convergence rate and good performance in parameter calibration.展开更多
Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. Th...Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions （PTFs） used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Cenuchten （VG） function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve （WRC）. A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques （statistical/neurM regression） have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error （RMSE）, index of agreement （d）, maximum absolute error （ME）, mean absolute error （MAE）, coefficient of determination （r2） and correlation coefficient （r）, have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.展开更多
文摘为满足电力系统实时动态观测的要求,需要精确估计电网络参数,基于电力系统的任何网络参数误差都会影响状态估计中的量测残差,通过对量测残差的分析提出了一个基于最小二乘的电网络参数误差估计方法。分两步完成电网络参数误差的估计:首先根据动态系统的量测方程,利用加权最小二乘法(weighted least squares method,以下简称WLS),同时引入网络节点虚拟量测量作为等式约束条件建立最优化模型,利用拉格朗日乘法提出量测残差的表达式,网络节点虚拟量测量的引入加强了算法的精确性和收敛速度;第二步首先利用递推最小二乘建立量测残差方程中偏差项的的迭代公式,电网络参数误差包含在量测残差方程的偏差项中,通过迭代得出一个无偏估计的初始状态量,最后求得电网络参数误差。IEEE9节点系统的仿真验证了算法的正确性和有效性。
文摘This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presented.Finally,a simulated adjustment problem is constructed to explain the method given in this paper.The results from the semiparametric model and G_M model are compared.The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method.
文摘In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the assumption that the distribution of observations is unimodal and symmetry, this method can give the estimates of the parametric. Finally, two simulated adjustment problem are constructed to explain this method. The new method presented in this paper shows an effective way of solving the problem; the estimated values are nearer to their theoretical ones than those by least squares adjustment approach.
文摘Feature initialization is an important issue in the monocular simultaneous locahzation ana mapping （SLAM） literature as the feature depth can not be obtained at one observation. In this paper, we present a new feature initialization method named modified homogeneous parameterization （MHP）, which allows undelayed initialization with scale invariant representation of point features located at various depths. The linearization error of the measurement equation is quantified using a depth estimation model and the feature initialization process is described. In order to verify the performance of the proposed method, the simulation is carried out. Results show that with the proposed method, the SLAM algorithm can achieve better consistency as compared with the existing inverse depth parameterization （IDP） method.
基金Project supported by the National Natural Science Foundation of China (No. 19631040).
文摘This paper studies the parameter estimation of one dimensional linear errors-in-variables(EV) models in the case that replicated observations are available in some experimental points.Asymptotic normality is established under mild conditions, and the parameters entering the asymptotic variance are consistently estimated to render the result useable in construction of large-sample confidence regions.
文摘We study the properties of the Lasso in the high-dimensional partially linear model where the number of variables in the linear part can be greater than the sample size.We use truncated series expansion based on polynomial splines to approximate the nonparametric component in this model.Under a sparsity assumption on the regression coefficients of the linear component and some regularity conditions,we derive the oracle inequalities for the prediction risk and the estimation error.We also provide sufficient conditions under which the Lasso estimator is selection consistent for the variables in the linear part of the model.In addition,we derive the rate of convergence of the estimator of the nonparametric function.We conduct simulation studies to evaluate the finite sample performance of variable selection and nonparametric function estimation.
基金supported by the National Natural Science Foundation of China (Grant No. 51279057)the Major Program of National Natural Science Foundation of China (Grant Nos. 51190090 and 51190091)+1 种基金the Ph.D. Programs Foundation of Ministry of Education of China (Grant No.20120094120018)the Fundamental Research Funds for the Central Universities of China (Grant No. 2012B00214)
文摘By analyzing the structure of the objective function based on error sum of squares and the information provided by the objective function, the essential problems in the current parameter estimation methods are summarized: (1) the information extracted from the objective function based on error sum of squares is unreasonable or even wrong for parameter estimation; and (2) the surface of the objective function based on error sum of squares is more complex than that of the parameter function, which indicates that the optimal parameter values should be searched on the surface of the parameter function instead of the objective function. This paper proposes the concept of sample intersection and demonstrates the uniqueness theorem of intersection point (namely the uniqueness of optimal parameter values). According to the characteristics of parameter function surface and Taylor series expansion, a parameter estimation method based on the sample intersection information extracted from parameter function surface (PFS method) was constructed. The results of theoretical analysis and practical application show that the proposed PFS method can avoid the problems in the current automatic parameter calibration, and has fast convergence rate and good performance in parameter calibration.
文摘Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions （PTFs） used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Cenuchten （VG） function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve （WRC）. A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques （statistical/neurM regression） have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error （RMSE）, index of agreement （d）, maximum absolute error （ME）, mean absolute error （MAE）, coefficient of determination （r2） and correlation coefficient （r）, have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.
