Numerical projection-based approach to nonlinear model reduction and identification

Jay H. Lee; Yangdong Pan; Suwhan Sung

Numerical projection-based approach to nonlinear model reduction and identification

Jay H. Lee, Yangdong Pan, Suwhan Sung

Department of Chemical Engineering

Purdue University

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

In this paper, we propose a general method for nonlinear model reduction and identification, inspired by the concept of subspace identification. We propose to use the artificial neural networks to find a nonlinear projection operator that serves to define the reduced state out of the full state or out of an input-output time series. We investigate the viability of the method for both deterministic and stochastic systems.

Original language	English
Pages (from-to)	1568-1572
Number of pages	5
Journal	Proceedings of the American Control Conference
Volume	3
State	Published - 1999
Event	Proceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA Duration: 2 Jun 1999 → 4 Jun 1999

Cite this

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title = "Numerical projection-based approach to nonlinear model reduction and identification",

abstract = "In this paper, we propose a general method for nonlinear model reduction and identification, inspired by the concept of subspace identification. We propose to use the artificial neural networks to find a nonlinear projection operator that serves to define the reduced state out of the full state or out of an input-output time series. We investigate the viability of the method for both deterministic and stochastic systems.",

author = "Lee, {Jay H.} and Yangdong Pan and Suwhan Sung",

year = "1999",

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journal = "Proceedings of the American Control Conference",

issn = "0743-1619",

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note = "Proceedings of the 1999 American Control Conference (99ACC) ; Conference date: 02-06-1999 Through 04-06-1999",

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T1 - Numerical projection-based approach to nonlinear model reduction and identification

AU - Lee, Jay H.

AU - Pan, Yangdong

AU - Sung, Suwhan

PY - 1999

Y1 - 1999

N2 - In this paper, we propose a general method for nonlinear model reduction and identification, inspired by the concept of subspace identification. We propose to use the artificial neural networks to find a nonlinear projection operator that serves to define the reduced state out of the full state or out of an input-output time series. We investigate the viability of the method for both deterministic and stochastic systems.

AB - In this paper, we propose a general method for nonlinear model reduction and identification, inspired by the concept of subspace identification. We propose to use the artificial neural networks to find a nonlinear projection operator that serves to define the reduced state out of the full state or out of an input-output time series. We investigate the viability of the method for both deterministic and stochastic systems.

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M3 - Conference article

AN - SCOPUS:0033283305

SN - 0743-1619

VL - 3

SP - 1568

EP - 1572

JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

T2 - Proceedings of the 1999 American Control Conference (99ACC)

Y2 - 2 June 1999 through 4 June 1999

ER -

Numerical projection-based approach to nonlinear model reduction and identification

Abstract

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