Identification of state models using principal components analysis

M. K. Hartnett; G. Lightbody; G. W. Irwin

doi:10.1016/S0169-7439(98)00184-1

Identification of state models using principal components analysis

M. K. Hartnett
, G. Lightbody
, G. W. Irwin

Queen's University Belfast

Research output: Contribution to journal › Article › peer-review

Abstract

A predictive state-space dynamic plant is identified using a two-stage approach based on principal components analysis. The procedure is applied to a simulated benchmark problem known as the overheads condensor reflux drum (OCRD) model, a non-linear multivariable plant with mixed dynamics. The identified model is validated against an independent test set and its step and frequency responses compared with a linearised analytical model of the OCRD.

Original language	English
Pages (from-to)	181-196
Number of pages	16
Journal	Chemometrics and Intelligent Laboratory Systems
Volume	46
Issue number	2
DOIs	https://doi.org/10.1016/S0169-7439(98)00184-1
Publication status	Published - 15 Mar 1999
Externally published	Yes

Keywords

PCA
Prediction
State-space modelling

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10.1016/S0169-7439(98)00184-1

Cite this

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title = "Identification of state models using principal components analysis",

abstract = "A predictive state-space dynamic plant is identified using a two-stage approach based on principal components analysis. The procedure is applied to a simulated benchmark problem known as the overheads condensor reflux drum (OCRD) model, a non-linear multivariable plant with mixed dynamics. The identified model is validated against an independent test set and its step and frequency responses compared with a linearised analytical model of the OCRD.",

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year = "1999",

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doi = "10.1016/S0169-7439(98)00184-1",

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AU - Lightbody, G.

AU - Irwin, G. W.

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N2 - A predictive state-space dynamic plant is identified using a two-stage approach based on principal components analysis. The procedure is applied to a simulated benchmark problem known as the overheads condensor reflux drum (OCRD) model, a non-linear multivariable plant with mixed dynamics. The identified model is validated against an independent test set and its step and frequency responses compared with a linearised analytical model of the OCRD.

AB - A predictive state-space dynamic plant is identified using a two-stage approach based on principal components analysis. The procedure is applied to a simulated benchmark problem known as the overheads condensor reflux drum (OCRD) model, a non-linear multivariable plant with mixed dynamics. The identified model is validated against an independent test set and its step and frequency responses compared with a linearised analytical model of the OCRD.

KW - PCA

KW - Prediction

KW - State-space modelling

UR - https://www.scopus.com/pages/publications/0033559992

U2 - 10.1016/S0169-7439(98)00184-1

DO - 10.1016/S0169-7439(98)00184-1

M3 - Article

AN - SCOPUS:0033559992

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SP - 181

EP - 196

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

IS - 2

ER -

Identification of state models using principal components analysis

Abstract

Keywords

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