Nonlinear dynamic system modeling based on neural state space model

Yongji Wang; Qing Wu; Hong Wang

doi:10.1117/12.441671

Nonlinear dynamic system modeling based on neural state space model

Yongji Wang, Qing Wu, Hong Wang

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

1 Scopus citations

Abstract

In this paper, an approach of nonlinear system modeling based on neural state space model is proposed. The neural state space model is of the quasi-linear characteristics of system, therefore, many linear system controller design approach can be extended to apply to the NNSP models. The EKF approach is adopted for parameter identification of neural state space models and a High-order correction method is then applied to test the validity of the neural state space model of nonlinear systems. The application of this method to dynamic modeling of typical chemical processes shows that the presented approach is effective.

Original language	English
Pages (from-to)	45-50
Number of pages	6
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4555
DOIs	https://doi.org/10.1117/12.441671
State	Published - 2001
Externally published	Yes
Event	Neural Network and Distributed Processing - Wuhan, China Duration: Oct 22 2001 → Oct 23 2001

Keywords

Continuous stirred tank reactor (CSTR)
Extended Kalman filter
Neural state space model

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10.1117/12.441671

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title = "Nonlinear dynamic system modeling based on neural state space model",

abstract = "In this paper, an approach of nonlinear system modeling based on neural state space model is proposed. The neural state space model is of the quasi-linear characteristics of system, therefore, many linear system controller design approach can be extended to apply to the NNSP models. The EKF approach is adopted for parameter identification of neural state space models and a High-order correction method is then applied to test the validity of the neural state space model of nonlinear systems. The application of this method to dynamic modeling of typical chemical processes shows that the presented approach is effective.",

keywords = "Continuous stirred tank reactor (CSTR), Extended Kalman filter, Neural state space model",

author = "Yongji Wang and Qing Wu and Hong Wang",

year = "2001",

doi = "10.1117/12.441671",

language = "English",

volume = "4555",

pages = "45--50",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "Society of Photo-Optical Instrumentation Engineers",

note = "Neural Network and Distributed Processing ; Conference date: 22-10-2001 Through 23-10-2001",

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TY - JOUR

T1 - Nonlinear dynamic system modeling based on neural state space model

AU - Wang, Yongji

AU - Wu, Qing

AU - Wang, Hong

PY - 2001

Y1 - 2001

N2 - In this paper, an approach of nonlinear system modeling based on neural state space model is proposed. The neural state space model is of the quasi-linear characteristics of system, therefore, many linear system controller design approach can be extended to apply to the NNSP models. The EKF approach is adopted for parameter identification of neural state space models and a High-order correction method is then applied to test the validity of the neural state space model of nonlinear systems. The application of this method to dynamic modeling of typical chemical processes shows that the presented approach is effective.

AB - In this paper, an approach of nonlinear system modeling based on neural state space model is proposed. The neural state space model is of the quasi-linear characteristics of system, therefore, many linear system controller design approach can be extended to apply to the NNSP models. The EKF approach is adopted for parameter identification of neural state space models and a High-order correction method is then applied to test the validity of the neural state space model of nonlinear systems. The application of this method to dynamic modeling of typical chemical processes shows that the presented approach is effective.

KW - Continuous stirred tank reactor (CSTR)

KW - Extended Kalman filter

KW - Neural state space model

UR - http://www.scopus.com/inward/record.url?scp=0035768069&partnerID=8YFLogxK

U2 - 10.1117/12.441671

DO - 10.1117/12.441671

M3 - Conference article

AN - SCOPUS:0035768069

SN - 0277-786X

VL - 4555

SP - 45

EP - 50

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Neural Network and Distributed Processing

Y2 - 22 October 2001 through 23 October 2001

ER -

Nonlinear dynamic system modeling based on neural state space model

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Keywords

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