Model reduction of uncertain discrete systems having interval uncertainties using genetic algorithms

Chen Chien Hsu; Tsung Chi Lu; Shih Chi Chang

Model reduction of uncertain discrete systems having interval uncertainties using genetic algorithms

Chen Chien Hsu^*, Tsung Chi Lu, Shih Chi Chang

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, an evolutionary approach is proposed to obtain a reduced-order discrete interval model for uncertain discrete-time systems having interval uncertainties based on resemblance of discrete sequence energy between the original and reduced systems. System performance of the discrete interval model obtained by using the proposed evolutionary approach is then verified based on time responses of the resulting model in comparison to existing methods to demonstrate the effectiveness of the proposed approach.

Original language	English
Pages	1440-1444
Number of pages	5
Publication status	Published - 2005
Externally published	Yes
Event	SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10

Other

Other	SICE Annual Conference 2005
Country/Territory	Japan
City	Okayama
Period	2005/08/08 → 2005/08/10

Keywords

Discrete sequence energy
Discrete-time systems
Genetic algorithms
Interval plant
Model reduction
Uncertain systems

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

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title = "Model reduction of uncertain discrete systems having interval uncertainties using genetic algorithms",

abstract = "In this paper, an evolutionary approach is proposed to obtain a reduced-order discrete interval model for uncertain discrete-time systems having interval uncertainties based on resemblance of discrete sequence energy between the original and reduced systems. System performance of the discrete interval model obtained by using the proposed evolutionary approach is then verified based on time responses of the resulting model in comparison to existing methods to demonstrate the effectiveness of the proposed approach.",

keywords = "Discrete sequence energy, Discrete-time systems, Genetic algorithms, Interval plant, Model reduction, Uncertain systems",

author = "Hsu, {Chen Chien} and Lu, {Tsung Chi} and Chang, {Shih Chi}",

year = "2005",

language = "English",

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T1 - Model reduction of uncertain discrete systems having interval uncertainties using genetic algorithms

AU - Hsu, Chen Chien

AU - Lu, Tsung Chi

AU - Chang, Shih Chi

PY - 2005

Y1 - 2005

N2 - In this paper, an evolutionary approach is proposed to obtain a reduced-order discrete interval model for uncertain discrete-time systems having interval uncertainties based on resemblance of discrete sequence energy between the original and reduced systems. System performance of the discrete interval model obtained by using the proposed evolutionary approach is then verified based on time responses of the resulting model in comparison to existing methods to demonstrate the effectiveness of the proposed approach.

AB - In this paper, an evolutionary approach is proposed to obtain a reduced-order discrete interval model for uncertain discrete-time systems having interval uncertainties based on resemblance of discrete sequence energy between the original and reduced systems. System performance of the discrete interval model obtained by using the proposed evolutionary approach is then verified based on time responses of the resulting model in comparison to existing methods to demonstrate the effectiveness of the proposed approach.

KW - Discrete sequence energy

KW - Discrete-time systems

KW - Genetic algorithms

KW - Interval plant

KW - Model reduction

KW - Uncertain systems

UR - http://www.scopus.com/inward/record.url?scp=33645277386&partnerID=8YFLogxK

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M3 - Paper

AN - SCOPUS:33645277386

SP - 1440

EP - 1444

T2 - SICE Annual Conference 2005

Y2 - 8 August 2005 through 10 August 2005

ER -

Model reduction of uncertain discrete systems having interval uncertainties using genetic algorithms

Abstract

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Keywords

ASJC Scopus subject areas

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