Evolutionary system identification in the time domain

K. C. Tan; Y. Li

doi:10.1243/0959651971539849

Evolutionary system identification in the time domain

K. C. Tan
, Y. Li

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

11 Citations (Scopus)

Abstract

This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.

Original language	English
Pages (from-to)	319-323
Journal	Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume	211
Issue number	5
DOIs	https://doi.org/10.1243/0959651971539849
Publication status	Published - Aug 1997
Externally published	Yes

Research Keywords

Genetic algorithms
System identification

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title = "Evolutionary system identification in the time domain",

abstract = "This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.",

keywords = "Genetic algorithms, System identification",

author = "Tan, \{K. C.\} and Y. Li",

year = "1997",

month = aug,

doi = "10.1243/0959651971539849",

language = "English",

volume = "211",

pages = "319--323",

journal = "Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering",

issn = "0959-6518",

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TY - JOUR

T1 - Evolutionary system identification in the time domain

AU - Tan, K. C.

AU - Li, Y.

PY - 1997/8

Y1 - 1997/8

N2 - This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.

AB - This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.

KW - Genetic algorithms

KW - System identification

UR - https://www.scopus.com/pages/publications/0031367923

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0031367923&origin=recordpage

U2 - 10.1243/0959651971539849

DO - 10.1243/0959651971539849

M3 - RGC 21 - Publication in refereed journal

SN - 0959-6518

VL - 211

SP - 319

EP - 323

JO - Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

JF - Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

IS - 5

ER -

Evolutionary system identification in the time domain

Abstract

Research Keywords

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