非齐次等式约束线性回归模型的条件岭估计的效率

利用文[7]提出的相对效率定义,研究文[8]、[9]非齐次等式约束线性回归模型回归系数的狭义条件岭估计和广义条件岭估计的效率,证明在一定条件下,狭义条件岭估计和广义条件岭估计的效率比约束最小二乘估计的效率高,并且广义条件岭估计的效率比狭义条件岭估计的效率高,从而进一步证明了在病态的模型中,有偏估计优于无偏估计。     

Vandermonde行列式的一类推广

本文研究了Vandermonde行列式的推广形式,给出了这类广义Vandermonde行列式满足的一些性质,并利用Laplace展开定理给出了这类行列式的计算公式。     

Vandermonde行列式的一类推广

本文研究了Vandermonde行列式的推广形式,给出了这类广义Vandermonde行列式满足的一些性质,并利用Laplace展开定理给出了这类行列式的计算公式。     

用matlab实现行列式和矩阵基本计算

本文介绍了行列式和矩阵的定义及基本计算,并给出了Matlab在行列式的计算和矩阵的线性运算中的应用。     

平衡损失函数下广义Pareto分布参数的Bayes估计的可容许性

对于一个参数进行估计来讲,不仅要考虑估计的精度,还要考虑所得到的估计对于模型拟合的优良程度。通常的损失函数无能为力,而平衡损失函数可以考虑到这两点,本文将在平衡误差损失函数下研究广义Pareto分布参数的Bayes估计问题。在平衡损失函数下导出了参数的Bayes估计并讨论了一类线性形式估计的可容许性和不可容许性。     

广义逆在解线性代数方程组中的应用

介绍了广义逆的基本概念，并讨论了广义逆在解线性代数方程组及线性最小二乘问题上的应用。     

平衡损失下一般随机效应模型中线性估计的可容许性

主要研究了一般随机效应模型中线性估计可容许性的问题.在平衡损失下,引入了齐次与非齐次线性估计类中线性估计可容许的概念,分别得到了齐次线性估计(非齐次线性估计)在齐次线性估计类(非齐次线性估计类)中可容许性的充分必要条件.     

分数阶广义积分-微分方程Riesz基的置信域估计

分数阶广义积分-微分方程Riesz基的置信域估计关系到方程是否存在稳定解,由分数阶广义积分-微分方程的初值解构成Riesz基,采用广义最小二乘估计(GLS)方法构成Riesz基的回归参数的置信域,广义积分-微分方程局部解存在性根据广义特征函数的分数阶非线性增长性约束条件进行验证。在重特征值的根子空间中通过Lyapunov泛函分析分数阶广义积分-微分方程Riesz基的置信域,通过计算最小二乘估计(OLS)估计的经验覆盖概率提高置信域估计的精度。     

回归系数一类线性估计的小样本性质

提出了线性模型中回归系数的一类线性估计.在均方误差矩阵(MSEM)准则和Pitman Closeness(PC)准则下,研究了这类线性估计相对于最小二乘(LS)估计的优良性.最后,讨论了当设计阵为非列满秩时,回归系数的可估函数的一类线性估计的优良性.     

关于高阶行列式计算的一些新方法的探讨

从行列式的定义、性质、展开定理、递推关系等得到计算行列式的几种方法，以体现行列式计算过程的灵活性和简洁性。     

An algebraic approach to on-line signal denoising and derivatives estimation

In this paper, a new algebraic approach to the on-line signal derivatives estimation is proposed. The proposed approach is based on the conversion of the truncated Taylor series expansion to the set of linearly independent equations regarding the signal derivatives. The nonhomogeneous parts of the obtained set of equations are convolution integrals, which can be transformed to the stable linear state-space filter realization. The proposed algebraic estimator provides stable convergence without the need for periodic re-initialization, as in the case of the conventional algebraic estimators. In contrast to the Taylor series-based tracking differentiators, the proposed estimator also provides an estimation of the arbitrary number of the higher-order signal derivatives. In addition, the tuning of the estimator parameters does not depends on the filter dimension. The efficiency of the proposed estimator is illustrated by the simulation examples and experimental results related to the monitoring of the surgical drilling process.     

Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems

In this paper a new integrated observer-based fault estimation and accommodation strategy for discrete-time piecewise linear (PWL) systems subject to actuator faults is proposed. A robust estimator is designed to simultaneously estimate the state of the system and the actuator fault. Then, the estimate of fault is used to compensate for the effect of the fault. By using the estimate of fault and the states, a fault tolerant controller using a PWL state feedback is designed. The observer-based fault-tolerant controller is obtained by the interconnection of the estimator and the state feedback controller. We show that separate design of the state feedback and the estimator results in the stability of the overall closed-loop system. In addition, the input-to-state stability (ISS) gain for the closed-loop system is obtained and a procedure for minimizing it is given. All of the design conditions are formulated in terms of linear matrix inequalities (LMI) which can be solved efficiently. Also, performance of the estimator and the state feedback controller are minimized by solving convex optimization problems. The efficiency of the method is demonstrated by means of a numerical example.     

Centralized fusion estimation over wireless sensor-actuator networks with unobservable packet dropouts

This paper studies centralized fusion estimation over a wireless sensor-actuator network, where packet dropouts cannot be observed by the fusion estimator. For such a system, we obtain an optimal linear fused estimation of system states, also known as optimal linear estimator. Then, we establish a necessary and sufficient condition for the stability of the optimal linear estimator. Finally, we show that the estimation performance is monotonically decreasing with respect to the observation packet-arrival rate. By analyzing a sequence that converges to the covariance of the optimal linear estimator, an analytical relationship between the estimation performance and the control packet-arrival rate is obtained. Simulation examples are given to illustrate the main results.     

Frequency-shifting-based stable on-line algebraic parameter identification of linear systems

In this paper a new approach to algebraic parameter identification of the linear SISO systems is proposed. The standard approach to the algebraic parameter identification is based on the algebraic derivatives in Laplace domain as the main tool for algebraic manipulations like elimination of the initial conditions and generation of linearly independent equations. This approach leads to the unstable time-varying state-space realization of the filters for the on-line parameter estimation. In this paper, the finite difference and shift operators in combination with the frequency-shifting property of Laplace transform is applied instead of algebraic derivatives. Resulting state-space realization of the estimator filters is asymptotically stable and doesn’t require switch-of mechanism to prevent overflow of the estimator variables. The proposed method is especially suitable for applications in closed-loop on-line identification where the stable behavior of the estimators is a necessary requirement. The efficiency of the proposed algorithm is illustrated on three simulation examples.     

A state estimation H∞ issue for discrete-time stochastic impulsive genetic regulatory networks in the presence of leakage,multiple delays and Markovian jumping parameters

In this work, we probes the stability results of H_∞ state estimation for discrete-time stochastic genetic regulatory networks with leakage, distributed delays, Markovian jumping parameters and impulsive effects. Here, we focus to evaluate the true absorption of mRNAs and proteins by calculating the H_∞ estimator in such a way that the estimation error dynamics is stochastically stable during the completion of the prescribed H_∞ disturbance attenuation level. In favor of decreasing the data communion in trouble, the H_∞ system accept and evaluate the outputs that are only transferred to the estimator when a certain case is acroses. Further, few sufficient conditions are formulated, by utilizing the Lyapunov–Krasovskii functional under which the estimation error system is stochastically stable and also satisfied the H_∞ attainment constraint. The estimator is obtained in terms of linear matrix inequalities (LMIs) and these LMIs are attainable, only if the estimator gains can be absolutely given. In addition to that, two numerical examples are exposed to establish the efficiency of our obtained results.     

Event-triggered interval-based state estimator for continuous-time linear systems

In this paper we propose an interval-based state estimator for continuous-time linear systems with discrete-time measurements using an event-triggered mechanism and an explicit reachability method. An output injection method combined with a state variables permutation procedure are applied to design the robust estimator. In addition, the convergence of the proposed set-membership state estimator and the existence of a lower bound on the inter-event times are shown. Throughout a numerical example, the performance of this estimator are illustrated and compared to related works.     

范德蒙行列式的应用探究

范德蒙行列式作为行列式计算的一种工具有着广泛的应用,本文探讨了范德蒙行列式在求解线性方程组、行列式计算、多项式理论以及微积分证明方面的应用,希望通过这些典型的例子的阐述掌握运用范德蒙行列式来解决问题的技巧和方法。     

Performance of the stochastic MV-PURE estimator in highly noisy settings

The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-PURE estimator) has been developed to provide linear estimation with robustness against high noise levels, imperfections in model knowledge, and ill-conditioned systems. In this paper, we investigate the theoretical performance of the stochastic MV-PURE estimator under varying levels of additive noise. We prove that the mean-square-error (MSE) of this estimator in the low signal-to-noise (SNR) region is much smaller than that obtained with its full-rank version, the minimum-variance distortionless estimator, and the gap becomes larger as the noise level increases. These results shed light on the excellent performance of the stochastic MV-PURE estimator in highly noisy settings obtained in simulations so far. Furthermore, we extend previous numerical simulations to show how the insight gained from the results of this paper can be used in practice.