A new relative efficiency of parameter estimation for generalized Gauss-Markov linear model was proposed. Its lower bound was also derived. Its properties were explored in comparison with three currently very popular ...A new relative efficiency of parameter estimation for generalized Gauss-Markov linear model was proposed. Its lower bound was also derived. Its properties were explored in comparison with three currently very popular relative efficiencies. The new relative efficiency not only reflects sensitively the error and loss caused by the substitution of the least square estimator for the best linear unbiased estimator, but also overcomes the disadvantage of weak dependence on the design matrix.展开更多
This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation an...This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation and in the meantime, preserves the same asymptotic normal distribution for the estimator, as in the ordinary minimum L_1-norm estimates.展开更多
In this paper, compression LS estimate (k) of the regression coefficient B isconsidered when the design matrix present ill-condition in multivariate linear model.The MSE (mean square error)of the estimate(k)=Ve...In this paper, compression LS estimate (k) of the regression coefficient B isconsidered when the design matrix present ill-condition in multivariate linear model.The MSE (mean square error)of the estimate(k)=Vec( (k))is less than theMSE of LS estimate β￣* of the regression coefficient β= Vec(B) by choosing the pa-rameter k. Admissibility , numerical stability and relative efficiency of (k)are proved. The method of determining k value for practical use is also suggested展开更多
For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimatin...For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimating f(x) and the consistency of the estimators is obtained.展开更多
Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness t...Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.展开更多
文摘A new relative efficiency of parameter estimation for generalized Gauss-Markov linear model was proposed. Its lower bound was also derived. Its properties were explored in comparison with three currently very popular relative efficiencies. The new relative efficiency not only reflects sensitively the error and loss caused by the substitution of the least square estimator for the best linear unbiased estimator, but also overcomes the disadvantage of weak dependence on the design matrix.
基金Research supported By AFOSC, USA, under Contract F49620-85-0008oy NNSFC of China.
文摘This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation and in the meantime, preserves the same asymptotic normal distribution for the estimator, as in the ordinary minimum L_1-norm estimates.
文摘In this paper, compression LS estimate (k) of the regression coefficient B isconsidered when the design matrix present ill-condition in multivariate linear model.The MSE (mean square error)of the estimate(k)=Vec( (k))is less than theMSE of LS estimate β￣* of the regression coefficient β= Vec(B) by choosing the pa-rameter k. Admissibility , numerical stability and relative efficiency of (k)are proved. The method of determining k value for practical use is also suggested
基金The project supported by National Natural Science Foundation of China Crant 18971061
文摘For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimating f(x) and the consistency of the estimators is obtained.
基金National Science Fund for Distinguished Young Scholars (50825502)
文摘Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.
