In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariate...In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.展开更多
A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condit...A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the as- similation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations re- spectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.展开更多
This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpol...This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpolation (OI), and 3D-Var. Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. The analysis package was applied to produce analyses at 6 h time interval for the period 1-11 August 2008. The period was selected for data availability and forty-one analyses were collected. The results show the validity of the different solutions, which can be chosen depending on the physical problem to solve and on the computational resources available. In particular, assuming the observations as the reference, all solutions show a decrease of the RMSE compared to the background. The decrease is consistent with the particular setting of the analysis system used in this paper. The comparison between different solutions shows that the SA converges to OI in few iterations, and that the SA solution with ten iteration is, in practice, equal to OI. Moreover, the 3D-Var method shows its potential to improve the analysis, once the horizontal and vertical length-scales and the background and observational errors are set optimally, because its solution may be sizeably different from two-dimensional methods.展开更多
为快速、准确地观测系统中的未知扰动及状态,提出一种有限时间线性扩张状态观测器(Finite-time linear extended state observer,FT-LESO),它具有期望的收敛性能且结构简单、易于设计.假设系统的状态无法量测,观测器设计问题转化为扰动...为快速、准确地观测系统中的未知扰动及状态,提出一种有限时间线性扩张状态观测器(Finite-time linear extended state observer,FT-LESO),它具有期望的收敛性能且结构简单、易于设计.假设系统的状态无法量测,观测器设计问题转化为扰动下的输出反馈控制问题.针对该问题,提出一种扰动下的有限时间线性输出反馈控制方法,得到控制器参数与闭环系统状态向量2-范数间的解析关系.在此基础上,提出有限时间线性扩张状态观测器,得到观测器参数与观测误差收敛速度及稳态观测误差间的解析关系,给出一充分条件保证观测误差有限时间有界、且能以不低于指数收敛的速度收敛到给定范围内,为观测器参数设计提供理论依据.通过数值仿真验证提出的观测器,仿真结果与理论分析相符,提出的观测器是有效的.展开更多
文摘In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 40275032, 40505005 and 40405019) Opening Foundation of Institute of Heavy Rain, CMA (Grant No. IHR2006G13)
文摘A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the as- similation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations re- spectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.
文摘This paper presents a general-purpose analysis package able to solve two- and three- dimensional analysis problems. The system can use the following methods of solution: Successive Approximation (SA), Optimal Interpolation (OI), and 3D-Var. Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. The analysis package was applied to produce analyses at 6 h time interval for the period 1-11 August 2008. The period was selected for data availability and forty-one analyses were collected. The results show the validity of the different solutions, which can be chosen depending on the physical problem to solve and on the computational resources available. In particular, assuming the observations as the reference, all solutions show a decrease of the RMSE compared to the background. The decrease is consistent with the particular setting of the analysis system used in this paper. The comparison between different solutions shows that the SA converges to OI in few iterations, and that the SA solution with ten iteration is, in practice, equal to OI. Moreover, the 3D-Var method shows its potential to improve the analysis, once the horizontal and vertical length-scales and the background and observational errors are set optimally, because its solution may be sizeably different from two-dimensional methods.
文摘为快速、准确地观测系统中的未知扰动及状态,提出一种有限时间线性扩张状态观测器(Finite-time linear extended state observer,FT-LESO),它具有期望的收敛性能且结构简单、易于设计.假设系统的状态无法量测,观测器设计问题转化为扰动下的输出反馈控制问题.针对该问题,提出一种扰动下的有限时间线性输出反馈控制方法,得到控制器参数与闭环系统状态向量2-范数间的解析关系.在此基础上,提出有限时间线性扩张状态观测器,得到观测器参数与观测误差收敛速度及稳态观测误差间的解析关系,给出一充分条件保证观测误差有限时间有界、且能以不低于指数收敛的速度收敛到给定范围内,为观测器参数设计提供理论依据.通过数值仿真验证提出的观测器,仿真结果与理论分析相符,提出的观测器是有效的.
