Online estimation of complexity using variable forgetting factor

Koichi Sugisaki; Hiromitsu Ohmori

doi:10.1109/SICE.2007.4420939

Online estimation of complexity using variable forgetting factor

Koichi Sugisaki, Hiromitsu Ohmori

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	SICE Annual Conference, SICE 2007
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/SICE.2007.4420939
Publication status	Published - 2007
Event	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: 2007 Sept 17 → 2007 Sept 20

Publication series

Name	Proceedings of the SICE Annual Conference

Other

Other	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
Country/Territory	Japan
City	Takamatsu
Period	07/9/17 → 07/9/20

Keywords

Approximate entropy
Complexity
Forgetting factor
Online
Recursive
Sample entropy

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2007.4420939

Cite this

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title = "Online estimation of complexity using variable forgetting factor",

abstract = "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.",

keywords = "Approximate entropy, Complexity, Forgetting factor, Online, Recursive, Sample entropy",

author = "Koichi Sugisaki and Hiromitsu Ohmori",

year = "2007",

doi = "10.1109/SICE.2007.4420939",

language = "English",

isbn = "4907764286",

series = "Proceedings of the SICE Annual Conference",

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note = "SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 ; Conference date: 17-09-2007 Through 20-09-2007",

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AU - Sugisaki, Koichi

AU - Ohmori, Hiromitsu

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Approximate entropy

KW - Complexity

KW - Forgetting factor

KW - Online

KW - Recursive

KW - Sample entropy

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DO - 10.1109/SICE.2007.4420939

M3 - Conference contribution

AN - SCOPUS:50249106239

SN - 4907764286

SN - 9784907764289

T3 - Proceedings of the SICE Annual Conference

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BT - SICE Annual Conference, SICE 2007

T2 - SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007

Y2 - 17 September 2007 through 20 September 2007

ER -

Online estimation of complexity using variable forgetting factor

Abstract

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Keywords

ASJC Scopus subject areas

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