广义离散随机线性系统自校正最优预报器
Self-Tuning Optimal Predictors for Singular Discrete Stochastic Linear Systems
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摘要: 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.
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关键词:
- 广义离散随机线性系统 /
- 自校正 /
- 预报器 /
- ARMA新息模型
Abstract: Using the modern time series analysis method, this paper deals with the optimal and adaptive state stimation for the singular discrete stochastic linear systems. The optimal predictor is presented by converting, the state estimation into the output prediction and noise estimation, and the asymptotic stability for the initial values of the optimal predictor is proved. The self-tuning predictor is also presented as the convariance matrixes are unknown in this paper. A simulation example shows its usefulness. -
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