Stochastic subspace identification of linear systems with observation outliers

被引：0
|
作者：
Almutawa, Jaafar [1 ]
机构：
[1] King Fahd Univ Petr & Minerals, Fac Dept Math & Stat, Dhahran 31261, Saudi Arabia
来源：
2013 21ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) | 2013年
关键词：
OUT DIAGNOSTICS; MODELS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose a diagnostic for the state space model fitting time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one. Thus we present a robust subspace system identification algorithm that is less sensitive to outliers. We give a numerical result to show effectiveness of the proposed method.
引用
收藏
页码：590 / 596
页数：7
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