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广义离散随机线性系统自校正最优预报器   总被引:5,自引:1,他引:4       下载免费PDF全文
运用现代时间序列分析[1]的方法研究广义离散随机线性系统最优及自适应状态估计. 将状态估计转化为输出预报和白噪声估计,从而提出了系统的最优预报器,并且证明最优预 报器对于初始值的选取渐近稳定.在噪声统计未知时提出了自校正预报器.仿真例子说明了 其有效性.  相似文献
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广义离散随机线性系统自校正状态估计   总被引:1,自引:0,他引:1  
本文运用现代时间序列分析的观点(1)研究广义离散随机线性系统的状态估计,提出了广义系统自校正最优滤波,平滑,预报。  相似文献
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本文运用现代时间序列分析^[1]的观点处理性离散时间系统自校正最优状态估计。运用新息理论和射影方法本文提出了一种新型最优滤器,在噪声统计未各时基于辨识ARMA新自模型提出了自校正滤波器,仿真例子说明新算法的效性。  相似文献
4.
By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, a unified and general information fusion steady-state Kalman filtering approach is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the filtering, smoothing, and prediction fusion problems for state or signal. The optimal fusion rule weighted by matrices is re-derived as a weighted least squares (WLS) fuser, and is reviewed. An optimal fusion rule weighted by diagonal matrices is presented, which is equivalent to the optimal fusion rule weighted by scalars for components, and it realizes a decoupled fusion. The new algorithms of the steady-state Kalman estimator gains are presented. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors by Lyapunov equations are presented. The exponential convergence of the iterative solution of Lyapunov equation is proved. It is proved that the optimal fusion estimators under three weighted fusion rules are locally optimal, but are globally suboptimal. The proposed steady-state Kalman fusers can reduce the on-line computational burden, and are suitable for real-time applications. A simulation example for the 3-sensor steady-state Kalman tracking fusion estimators shows their effectiveness and correctness, and gives the accuracy comparison of the fusion rules.  相似文献
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This paper is concerned with steady-state risk-sensitive filtering, prediction and smoothing problems for discrete-time singular systems. It is shown that a risk-sensitive estimator can be obtained by ensuring the minimum of an indefinite quadratic form to be maximum (minimum) when the risk-sensitivity parameter θ is negative (positive). An auxiliary state-space signal model and an innovation sequence in Krein space are introduced to simplify the derivation of the estimator. The estimator is calculated based on one J-spectral factorization for risk-seeking (θ<0) or one H2 spectral factorization for risk-averse (θ>0). A numerical example is given to demonstrate the applicability of the result.  相似文献
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基于经典稳态Kalman滤波理论, 对带白色和有色观测噪声系统提出了设计最优Wiener状态估值器的新方法. 通过稳态Kalman滤波器建立ARMA新息模型, 由稳态最优非递推Kalman状态估值器的递推变形引出Wiener状态估值器, 可统一处理滤波、预报和平滑问题, 它们具有状态解耦的ARMA递推形式, 且具有渐近稳定性和最优性, 仿真结果表明了算法的有效性.  相似文献
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提出了用多维Gevers-Wouters(G-W)算法得到稳定的滑动平均(MA)过程的一个频域充分条件,并给出了在构造ARMA新息模型中的应用,给出了保证ARMA新息模型的MA多项式矩阵稳定的一个时域充分条件,仿真结果表明,多维G-V算法具有快速收敛的性质。  相似文献
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