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估计误差方差
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  estimation error variance
    The problem of state estimation for linear discrete-time stochastic systems with estimation error variance and H_∞ morm constraints is considered. The aim of this problem is to design the filter gain such that the steady-state estimation error variance for each state is not greater than the prespecified value,and the transfer function from disturbances to error states satisfies the prespecified H_∞ norm constraint,simultenous-ly.
    考虑了线性离散随机系统在估计误差方差和H_∞范数约束下的状态估计问题,即设计滤波增益,使系统每个状态分量的估计误差方差的稳态值不大于预先指定值,同时从扰动到误差状态输出的传递函数也满足预先给定的H_∞范数约束。
短句来源
    This paper considers the problem of robust constrained variance state estimation for linear continuous , stochastic systems with structured parameter perturbations. The problem is the design of filter gains such that the steady-state value of the estimation error variance for each state of the perturbed. system is not more than the prespecified value, and .
    本文考虑含结构参数扰动的线性连续随机系统的鲁棒约束方差状态估计问题,即设计滤波增益,使受扰系统每个状态分量的估计误差方差的稳态值不大于预先给定值,同时滤波矩阵具有期望的稳定裕度.本文给出了上述性能鲁棒滤波增益的存在条件及解析表达式,并提供了相应的数值例子.
短句来源
    The problem of robust variance - constrained state estimation is studied for linear discrete - time stochastic systems with uncertainty in the state matrix. The aim is to design a filtering gain such that the steady -state estimation error variance for each state of the perturbed system is not more than the respective prespecified value.
    考虑状态矩阵中含不确定性的线性离散随机系统的鲁棒约束方差状态估计问题,即设计滤波增益,使摄动系统每个状态分量的估计误差方差的稳态值不大于各自预先给定值。
短句来源
  “估计误差方差”译为未确定词的双语例句
    The problem of robust H ∞ filtering for the Delta operator formulated uncertain discrete time systems with error variance constraints is considered.
    研究Delta算子不确定系统在稳态估计误差方差约束下的鲁棒H∞ 滤波问题 .
短句来源
    First, we focus on the design of Kalman filters for stochastic 2-D FMII models. A generalized Kalman filter is proposed to stochastic 2-D FMII models that can minimize the variance of the estimation error of the state vectors. It is an extension of the well-known 1-D Kalman filter, which may avoid the dimension disaster in other relevant approaches.
    在随机2-D线性离散系统的Kalman滤波、白噪声估值器部分,将1-DKalman滤波推广到在状态和测量方程中均带有Gaussian白噪声的2-DFMII模型,使得状态向量的估计误差方差达到最小,同时克服了维数灾难,它是将Kalman滤波器向2-D离散系统的直接推广。
短句来源
    1. Proportion-Differential filtering(PDF)is presented for nonlinear discrete time-varying stochastic systems. PDF is derived not only from taking into account minimizing variance of state estimation error, but regarding its rate of change. Therefore, it has higher estimating precision and stability than the extended Kalman filtering.
    1.提出非线性离散随机系统比例微分滤波,该滤波方法联合考虑极小化状态估计误差方差和状态误差变化率的方差,较扩展Kalman滤波稳定性强,提高了估计的精度.
短句来源
    After the correlativeness of Legendre-polynomial approx ima ting values of continuous Wiener process and the biasedness of the least-squares estimation for continuous linear systems disturbed with the process via the polynomial are discussed, Markov method with unbiased consistence and the minimum variance of the estimated errors is proposed. Finally, simulation results of the method are given.
    本文在讨论连续Wiener过程的Legendre多项式逼近值的相关性和Wiener过程扰动下连续线性系统基于该正交多项式的最小二乘(LS)估计有偏性后,提出了无偏一致的且参数估计误差方差最小的Markov估计(最小方差估计)算法,并给出本文方法的仿真结果。
短句来源
    Firstly,the least-squares parameter estimation method for linear continuous regressive models disturbed with Wiener process via Chebyshev polynomial approximation is proposed,then the correlativeness of the polynomial approximating values of Wiener process is discussed. Based on the correlative results of the approximating values of Wiener process,Markov parameter estimation algorithm which can give an unbiased consistent estimated values with the minimum covariance of the parameter estimated error is proposed.
    文中先提出了基于Chebyshev多项式逼近,有连续Wiener过程扰动的线性连续回归模型最小二乘参数估计算法,然后讨论了Wiener过程的Chebysbev多项式逼近值的相关性,在此基础上,提出了能获得参数估计误差方差为最小的Markov参数估计(最小方基估计)算法。
短句来源
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  estimation error variance
Using this approach, the optimal design of the network can be attained by minimising the related estimation error variance.
      
We have studied the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints.
      


After the correlativeness of Legendre-polynomial approx ima ting values of continuous Wiener process and the biasedness of the least-squares estimation for continuous linear systems disturbed with the process via the polynomial are discussed, Markov method with unbiased consistence and the minimum variance of the estimated errors is proposed. Finally, simulation results of the method are given.

本文在讨论连续Wiener过程的Legendre多项式逼近值的相关性和Wiener过程扰动下连续线性系统基于该正交多项式的最小二乘(LS)估计有偏性后,提出了无偏一致的且参数估计误差方差最小的Markov估计(最小方差估计)算法,并给出本文方法的仿真结果。

Firstly,the least-squares parameter estimation method for linear continuous regressive models disturbed with Wiener process via Chebyshev polynomial approximation is proposed,then the correlativeness of the polynomial approximating values of Wiener process is discussed.Based on the correlative results of the approximating values of Wiener process,Markov parameter estimation algorithm which can give an unbiased consistent estimated values with the minimum covariance of the parameter estimated error is proposed.These...

Firstly,the least-squares parameter estimation method for linear continuous regressive models disturbed with Wiener process via Chebyshev polynomial approximation is proposed,then the correlativeness of the polynomial approximating values of Wiener process is discussed.Based on the correlative results of the approximating values of Wiener process,Markov parameter estimation algorithm which can give an unbiased consistent estimated values with the minimum covariance of the parameter estimated error is proposed.These estimation methods proposed in this ppper are discussed,also,to apply on the parameter estimation problem of stochastic dynamical continuous systems in control field. Finally,the computer simulation results show the effectiveness of these parameter methods.

文中先提出了基于Chebyshev多项式逼近,有连续Wiener过程扰动的线性连续回归模型最小二乘参数估计算法,然后讨论了Wiener过程的Chebysbev多项式逼近值的相关性,在此基础上,提出了能获得参数估计误差方差为最小的Markov参数估计(最小方基估计)算法。文中还将所提出的估计方法推广至控制领域中所讨论的随机连续动态系统的参数估计中,计算机仿真结果显示了本文方法的有效性。

The problem of state estimation for linear discrete-time stochastic systems with estimation error variance and H_∞ morm constraints is considered.The aim of this problem is to design the filter gain such that the steady-state estimation error variance for each state is not greater than the prespecified value,and the transfer function from disturbances to error states satisfies the prespecified H_∞ norm constraint,simultenous-ly.The conditions for the existence of desired filter gains are given and the analytical...

The problem of state estimation for linear discrete-time stochastic systems with estimation error variance and H_∞ morm constraints is considered.The aim of this problem is to design the filter gain such that the steady-state estimation error variance for each state is not greater than the prespecified value,and the transfer function from disturbances to error states satisfies the prespecified H_∞ norm constraint,simultenous-ly.The conditions for the existence of desired filter gains are given and the analytical expression of such filter gains is also presented.An example concerning the problem of tracking maneuverable targets is provided to demonstrate the directness and effective-ness of the present design methodology.

考虑了线性离散随机系统在估计误差方差和H_∞范数约束下的状态估计问题,即设计滤波增益,使系统每个状态分量的估计误差方差的稳态值不大于预先指定值,同时从扰动到误差状态输出的传递函数也满足预先给定的H_∞范数约束。给出了期望滤波增益的存在条件及其解析表达式,并用数值算例说明了本文设计方法的直接性和有效性。

 
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