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A New Method for Identifying the Model Error of Adjust ment System 被引量:3
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作者 TAO Benzao ZHANG Chaoyu 《Geo-Spatial Information Science》 2005年第3期189-192,共4页
Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment... Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. 展开更多
关键词 adjustment model model error model error estimation model error identification
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Neural network based method for compensating model error 被引量:2
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作者 胡伍生 孙璐 《Journal of Southeast University(English Edition)》 EI CAS 2009年第3期400-403,共4页
Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network (call... Two traditional methods for compensating function model errors, the method of adding systematic parameters and the least-squares collection method, are introduced. A proposed method based on a BP neural network (called the H-BP algorithm) for compensating function model errors is put forward. The function model is assumed as y =f(x1, x2,… ,xn), and the special structure of the H-BP algorithm is determined as ( n + 1) ×p × 1, where (n + 1) is the element number of the input layer, and the elements are xl, x2,…, xn and y' ( y' is the value calculated by the function model); p is the element number of the hidden layer, and it is usually determined after many tests; 1 is the dement number of the output layer, and the element is △y = y0-y'(y0 is the known value of the sample). The calculation steps of the H-BP algorithm are introduced in detail. And then, the results of three methods for compensating function model errors from one engineering project are compared with each other. After being compensated, the accuracy of the traditional methods is about ± 19 mm, and the accuracy of the H-BP algorithm is ± 4. 3 mm. It shows that the proposed method based on a neural network is more effective than traditional methods for compensating function model errors. 展开更多
关键词 model error neural network BP algorithm compen- sating
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An approach to estimating and extrapolating model error based on inverse problem methods:towards accurate numerical weather prediction 被引量:4
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作者 胡淑娟 邱春雨 +3 位作者 张利云 黄启灿 于海鹏 丑纪范 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第8期669-677,共9页
Model error is one of the key factors restricting the accuracy of numerical weather prediction (NWP). Considering the continuous evolution of the atmosphere, the observed data (ignoring the measurement error) can ... Model error is one of the key factors restricting the accuracy of numerical weather prediction (NWP). Considering the continuous evolution of the atmosphere, the observed data (ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers' equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP. 展开更多
关键词 numerical weather prediction model error past data inverse problem
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An Online Model Correction Method Based on an Inverse Problem:Part I—Model Error Estimation by Iteration 被引量:3
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作者 XUE Haile SHEN Xueshun CHOU Jifan 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2015年第10期1329-1340,共12页
Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the pred... Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model (model error, denoted as ME). In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature (SST) were both based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS. 展开更多
关键词 model error past data inverse problem error estimation model correction GRAPES-GFS
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Approach for wideband direction-of-arrival estimation in the presence of array model errors 被引量:3
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作者 Chen Deli Zhang Cong Tao Huamin Lu Huanzhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第1期69-75,共7页
The presence of array imperfection and mutual coupling in sensor arrays poses several challenges for development of effective algorithms for the direction-of-arrival (DOA) estimation problem in array processing. A c... The presence of array imperfection and mutual coupling in sensor arrays poses several challenges for development of effective algorithms for the direction-of-arrival (DOA) estimation problem in array processing. A correlation domain wideband DOA estimation algorithm without array calibration is proposed, to deal with these array model errors, using the arbitrary antenna array of omnidirectional elements. By using the matrix operators that have the memory and oblivion characteristics, this algorithm can separate the incident signals effectively. Compared with other typical wideband DOA estimation algorithms based on the subspace theory, this algorithm can get robust DOA estimation with regard to position error, gain-phase error, and mutual coupling, by utilizing a relaxation technique based on signal separation. The signal separation category and the robustness of this algorithm to the array model errors are analyzed and proved. The validity and robustness of this algorithm, in the presence of array model errors, are confirmed by theoretical analysis and simulation results. 展开更多
关键词 DIRECTION-OF-ARRIVAL array model errors wideband.
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Recursive weighted least squares estimation algorithm based on minimum model error principle 被引量:2
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作者 雷晓云 张志安 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2021年第2期545-558,共14页
Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri... Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness. 展开更多
关键词 Minimum model error Weighted least squares method State estimation Invariant embedding method Nonlinear recursive estimate
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An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction
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作者 XUE Haile SHEN Xueshun CHOU Jifan 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2015年第11期1493-1503,共11页
An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors (MEs) in past intervals. Given t... An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors (MEs) in past intervals. Given the analyses, the ME in each interval (6 h) between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year (2001-2005) GRAPES- GFS (Global Forecast System of the Global and Regional Assimilation and Prediction System) error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient. 展开更多
关键词 model error past data inverse problem error estimation model correction GRAPES-GFS
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Application of Backward Nonlinear Local Lyapunov Exponent Method to Assessing the Relative Impacts of Initial Condition and Model Errors on Local Backward Predictability
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作者 Xuan LI Jie FENG +1 位作者 Ruiqiang DING Jianping LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2021年第9期1486-1496,共11页
Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In thi... Initial condition and model errors both contribute to the loss of atmospheric predictability.However,it remains debatable which type of error has the larger impact on the prediction lead time of specific states.In this study,we perform a theoretical study to investigate the relative effects of initial condition and model errors on local prediction lead time of given states in the Lorenz model.Using the backward nonlinear local Lyapunov exponent method,the prediction lead time,also called local backward predictability limit(LBPL),of given states induced by the two types of errors can be quantitatively estimated.Results show that the structure of the Lorenz attractor leads to a layered distribution of LBPLs of states.On an individual circular orbit,the LBPLs are roughly the same,whereas they are different on different orbits.The spatial distributions of LBPLs show that the relative effects of initial condition and model errors on local backward predictability depend on the locations of given states on the dynamical trajectory and the error magnitudes.When the error magnitude is fixed,the differences between the LBPLs vary with the locations of given states.The larger differences are mainly located on the inner trajectories of regimes.When the error magnitudes are different,the dissimilarities in LBPLs are diverse for the same given state. 展开更多
关键词 Initial condition model errors error magnitude error location LBPL
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The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model
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作者 ZHAO Peng DUAN Wan-Suo 《Atmospheric and Oceanic Science Letters》 CSCD 2014年第5期447-452,共6页
Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation (CNOP)-type initial errors and nonlinear forcing singular vector... Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation (CNOP)-type initial errors and nonlinear forcing singular vector (NFSV)- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error (NFSV-type tendency error) represents the initial errors (model errors) that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model (perfect initial conditions) scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events. 展开更多
关键词 PREDICTABILITY initial error model error optimal perturbation
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FORMING DYNAMIC EQUATIONS OF ELASTIC LINKAGE AND INVESTIGATION OF MODEL ERROR
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作者 Zou Huijun(Shanghai Jiaotong University)Zhang Mingli(Shanghai Maritime University) 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 1995年第1期30-37,14,共17页
Dynamic equations of elastic linkage are formed by means of Kane equation and finitemethod. Eight main influential factors on the dynamic response of elastic linkage are considered inthese equations. Model error cause... Dynamic equations of elastic linkage are formed by means of Kane equation and finitemethod. Eight main influential factors on the dynamic response of elastic linkage are considered inthese equations. Model error caused by the eight factors are investigated. Some useful conclusionsabout model error are derived from theoretical analysis and the numerical calculation of twenty-sixexamples. 展开更多
关键词 KED model error
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R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates
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作者 André Beauducel 《Open Journal of Statistics》 2024年第1期38-54,共17页
Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- a... Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis. 展开更多
关键词 R-Factor Analysis Q-Factor Analysis Loading Bias model error Multivariate Kurtosis
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Positional Error Model of Line Segments with Modeling and Measuring Errors Using Brownian Bridge 被引量:1
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作者 Xiaohua TONG Lejingyi ZHOU Yanmin JIN 《Journal of Geodesy and Geoinformation Science》 CSCD 2023年第2期1-10,共10页
Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science.There are not only the measuring errors of the endpoints but also... Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science.There are not only the measuring errors of the endpoints but also the modeling errors between the line segments and the actual geographical features.This paper presents a Brownian bridge error model for line segments combining both the modeling and measuring errors.First,the Brownian bridge is used to establish the position distribution of the actual geographic feature represented by the line segment.Second,an error propagation model with the constraints of the measuring error distribution of the endpoints is proposed.Third,a comprehensive error band of the line segment is constructed,wherein both the modeling and measuring errors are contained.The proposed error model can be used to evaluate line segments’overall accuracy and trustability influenced by modeling and measuring errors,and provides a comprehensive quality indicator for the geospatial data. 展开更多
关键词 spatial data line segment modeling error measuring error Brownian bridge
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Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model 被引量:2
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作者 Leyang Wang Shuhao Han 《Geodesy and Geodynamics》 EI CSCD 2023年第4期385-391,共7页
To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a deriv... To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models. 展开更多
关键词 Mixed additive and multiplicative random error model Parameter estimation Least squares Cat swarm optimization Powell method
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A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal,Inverse-Kinematic and Geometric-Constraint Error Models
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作者 Haiyu Wu Lingyu Kong +2 位作者 Qinchuan Li Hao Wang Genliang Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第5期206-230,共25页
Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically afects the accuracy, reliability, and stability of identifcation results. In this paper, a c... Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically afects the accuracy, reliability, and stability of identifcation results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of diferent error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identifcation accuracy, the impact of noise uncertainty, and parameter identifability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the infuence of the S joint, identifcation with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the diferences among the three error models. The results show that the residual errors of this machine tool are signifcantly improved according to the identifed parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The fndings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators. 展开更多
关键词 Kinematic calibration Parallel manipulator error modeling Product of exponential(POE)
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Error modeling of 3-RSR parallel robot based on D-H transformation matrix 被引量:3
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作者 李瑞琴 杨斌 《Journal of Measurement Science and Instrumentation》 CAS 2014年第3期53-59,2,共7页
By selecting any one limb of 3-RSR parallel robot as a research object, the paper establishes a position and orienta- tion relationship matrix between the moving platform and the base by means of Denavit-Hartenberg (... By selecting any one limb of 3-RSR parallel robot as a research object, the paper establishes a position and orienta- tion relationship matrix between the moving platform and the base by means of Denavit-Hartenberg (D-H) transformation matrix. The error mapping model is derived from original error to the error of the platform by using matrix differential method. This model contains all geometric original errors of the robot. The nonlinear implicit function relation between po- sition and orientation error of the platform and the original geometric errors is simplified as a linear explicit function rela- tion. The results provide a basis for further studying error analysis and error compensation. 展开更多
关键词 error modeling 3-RSR parallel robot Denavit-Hartenberg (D-H) transformation matrix
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Time-Dependent Nonlinear Forcing Singular Vector-Type Tendency Error of the Zebiak-Cane Model 被引量:1
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作者 ZHAO Peng DUAN Wan-Suo 《Atmospheric and Oceanic Science Letters》 CSCD 2014年第5期395-399,共5页
Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector (NFSV)-type tendency errors with components of 4 and 12 (denoted by NFSV-4 and NFSV-12) are calculated for predetermined El Nifio... Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector (NFSV)-type tendency errors with components of 4 and 12 (denoted by NFSV-4 and NFSV-12) are calculated for predetermined El Nifio events and compared with the constant NFSV (denoted by NFSV-1) from their patterns and resultant prediction errors. Specifically, NFSV-1 has a zonal dipolar sea surface temperature anomaly (SSTA) pattern with negative anomalies in the equatorial eastern Pacific and positive anomalies in the equatorial central-western Pa- cific. Although the first few components in NFSV-4 and NFSV-12 present patterns similar to NFSV-1, they tend to extend their dipoles farther westward; meanwhile, the positive anomalies gradually cover much smaller regions with the lag times. In addition, the authors calculate the predic- tion errors caused by the three kinds of NFSVs, and the results indicate that the prediction error induced by NFSV-12 is the largest, followed by the NFSV-4. However, when compared with the prediction errors caused by random tendency errors, the NFSVs generate significantly larger prediction errors. It is therefore shown that the spatial structure of tendency errors is important for producing large prediction errors. Furthermore, in exploring the tendency errors that cause the largest prediction error for E1 Nifio events, the timedependent NFSV should be evaluated. 展开更多
关键词 PREDICTABILITY model error optimal perturbation
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BDS satellite clock offset prediction based on a semiparametric adjustment model considering model errors 被引量:4
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作者 Xiong Yan Wentao Li +1 位作者 Yufeng Yang Xiong Pan 《Satellite Navigation》 2020年第1期113-125,共13页
In view of the influence of model errors in conventional BeiDou prediction models for clock offsets,a semiparametric adjustment model for BeiDou Navigation Satellite System(BDS)clock offset prediction that considers m... In view of the influence of model errors in conventional BeiDou prediction models for clock offsets,a semiparametric adjustment model for BeiDou Navigation Satellite System(BDS)clock offset prediction that considers model errors is proposed in this paper.First,the model errors of the conventional BeiDou clock offset prediction model are analyzed.Additionally,the relationship among the polynomial model,polynomial model with additional periodic term correction,and its periodic correction terms is explored in detail.Second,considering the model errors,combined with the physical relationship between phase,frequency,frequency drift,and its period in the clock sequence,the conventional clock offset prediction model is improved.Using kernel estimation and comprehensive least squares,the corresponding parameter solutions of the prediction model and the estimation of its model error are derived,and the dynamic error correction of the clock sequence model is realized.Finally,the BDS satellite precision clock data provided by the IGS Center of Wuhan University with a sampling interval of 5 min are used to compare the proposed prediction method with commonly used methods.Experimental results show that the proposed prediction method can better correct the model errors of BDS satellite clock offsets,and it can effectively overcome the inaccuracies of clock offset correction.The average forecast accuracies of the BeiDou satellites at 6,12,and 24 h are 27.13%,37.71%,and 45.08%higher than those of the conventional BeiDou clock offset forecast models;the average model improvement rates are 16.92%,20.96%,and 28.48%,respectively.In addition,the proposed method enhances the existing BDS satellite prediction method for clock offsets to a certain extent. 展开更多
关键词 BDS Satellite clock offset model errors Semiparametric adjustment model Clock offset forecast
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Revisiting Total Model Errors and Model Validation
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作者 LJUNG Lennart 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2021年第5期1598-1603,共6页
The paper contains a discussion of earlier work on Total Model Errors and Model Validation.It is maintained that the recent change of paradigm to kernel based system identification has also affected the basis for(and ... The paper contains a discussion of earlier work on Total Model Errors and Model Validation.It is maintained that the recent change of paradigm to kernel based system identification has also affected the basis for(and interest in)giving bounds for the total model error. 展开更多
关键词 BIAS kernel methods model errors REGULARIZATION system identification variance
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Non-gaussian Test Models for Prediction and State Estimation with Model Errors
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作者 Michal BRANICKI Nan CHEN Andrew J.MAJDA 《Chinese Annals of Mathematics,Series B》 SCIE CSCD 2013年第1期29-64,共36页
Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiqui- tous in applications in contemporary science and engin... Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiqui- tous in applications in contemporary science and engineering where the statistical ensemble prediction and the real time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test models have a crucial role to provide firm mathematical underpinning or new algorithms for vastly more complex scien- tific phenomena. Here, a class of statistically exactly solvable non-Gaussian test models is introduced, where a generalized Feynman-Ka~ formulation reduces the exact behavior of conditional statistical moments to the solution to inhomogeneous Fokker-Planck equations modified by linear lower order coupling and source terms. This procedure is applied to a test model with hidden instabilities and is combined with information theory to address two important issues in the contemporary statistical prediction of turbulent dynamical systems: the coarse-grained ensemble prediction in a perfect model and the improving long range forecasting in imperfect models. The models discussed here should be use- ful for many other applications and algorithms for the real time prediction and the state estimation. 展开更多
关键词 PREDICTION model error Information theory Feynman-Kac framework Fokker planck Turbulent dynamical systems
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Error Modeling and Sensitivity Analysis of a Parallel Robot with SCARA(Selective Compliance Assembly Robot Arm) Motions 被引量:18
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作者 CHEN Yuzhen XIE Fugui +1 位作者 LIU Xinjun ZHOU Yanhua 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2014年第4期693-702,共10页
Parallel robots with SCARA(selective compliance assembly robot arm) motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parall... Parallel robots with SCARA(selective compliance assembly robot arm) motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration. 展开更多
关键词 parallel robot selective compliance assembly robot arm(SCARA) motions error modeling sensitivity analysis parallelogram structure
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