Identification of wiener model using genetic algorithms

Hussain N. Al-Duwaish

doi:10.1109/IEEEGCC.2009.5734311

Identification of wiener model using genetic algorithms

Hussain N. Al-Duwaish

Department of Electrical Engineering

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

3 Scopus citations

Abstract

This paper investigates the use of genetic algorithms in the identification of Wiener model. The parameters describing the linear system and the static nonlinearity are estimated from input-output measurements by minimizing the error between the actual and identified systems. Using genetic algorithms, systems with non-minimum phase characteristics can be identified. Simulation results reveal the effectiveness and robustness of the proposed identification algorithm.

Original language	English
Title of host publication	2009 5th IEEE GCC Conference and Exhibition, GCC 2009
DOIs	https://doi.org/10.1109/IEEEGCC.2009.5734311
State	Published - 2009

Publication series

Name	2009 5th IEEE GCC Conference and Exhibition, GCC 2009

Keywords

Genetic algorithms
Non-minimum phase
Wiener model

ASJC Scopus subject areas

Energy Engineering and Power Technology
Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/IEEEGCC.2009.5734311

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title = "Identification of wiener model using genetic algorithms",

abstract = "This paper investigates the use of genetic algorithms in the identification of Wiener model. The parameters describing the linear system and the static nonlinearity are estimated from input-output measurements by minimizing the error between the actual and identified systems. Using genetic algorithms, systems with non-minimum phase characteristics can be identified. Simulation results reveal the effectiveness and robustness of the proposed identification algorithm.",

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AB - This paper investigates the use of genetic algorithms in the identification of Wiener model. The parameters describing the linear system and the static nonlinearity are estimated from input-output measurements by minimizing the error between the actual and identified systems. Using genetic algorithms, systems with non-minimum phase characteristics can be identified. Simulation results reveal the effectiveness and robustness of the proposed identification algorithm.

KW - Genetic algorithms

KW - Non-minimum phase

KW - Wiener model

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U2 - 10.1109/IEEEGCC.2009.5734311

DO - 10.1109/IEEEGCC.2009.5734311

M3 - Conference contribution

AN - SCOPUS:79953872749

SN - 9781424438853

T3 - 2009 5th IEEE GCC Conference and Exhibition, GCC 2009

BT - 2009 5th IEEE GCC Conference and Exhibition, GCC 2009

ER -

Identification of wiener model using genetic algorithms

Abstract

Publication series

Keywords

ASJC Scopus subject areas

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Cite this