Set-membership adaptive filtering

被引：0
|
作者：
Huang, YF [1 ]
机构：
[1] Univ Notre Dame, Dept Elect Engn, Notre Dame, IN 46556 USA
来源：
STABILITY AND CONTROL OF DYNAMICAL SYSTEMS WITH APPLICATIONS: A TRIBUTE TO ANTHONY N. MICHEL | 2003年
关键词：
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：
Based on a bounded-error assumption, set-membership adaptive filtering (SMAF) offers a viable alternative to traditional adaptive filtering techniques that are aimed to minimize an ensemble (or a time) average of the errors. This chapter presents an overview of the principles of SMAF, its features, and some applications. Highlighting the novel features of SMAF includes data-dependent selective update of the parameter estimates. Simulation experiences have shown that the SMAF algorithms normally use less than 20% of the data for updating parameter estimates while achieving performance comparable to traditional algorithms.
引用
收藏
页码：255 / 267
页数：13
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