Online estimation of complexity using variable forgetting factor

被引：0

作者：

Sugisaki, Koichi ^{[1
]}

Ohmori, Hiromitsu ^{[2
]}

机构：

[1] Keio Univ, Sch Integrated Design Engn, Kanagawa, Japan

[2] Keio Univ, Dept Syst Design Engn, Kanagawa, Japan

来源：

PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8 | 2007年

关键词：

complexity; Approximate Entropy; Sample Entropy; online; recursive; forgetting factor;

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：

Recently, the utility of Sample Entropy(SampEn) as a complexity measure was shown via applying to time series data generated from in a variety of systems. However, online estimation method of SampEn index has not been developed yet. If SampEn can be estimated online, we can apply this index to time-varying system. In this paper, we developed the recursive SampEn algorithm to estimate the changes of system complexity online. In additon, we verified the utility of this algorithm by simulations. Consequently, we assure that this algorithm can be applied to time series generated from in a variety of time-varying system potentially.

引用

页码：1 / +

页数：2

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