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Proceedings Paper

Nonlinear dynamic system modeling based on neural state space model
Author(s): Yongji Wang; Qing Wu; Hong Wang
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Paper 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.

Paper Details

Date Published: 20 September 2001
PDF: 6 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441671
Show Author Affiliations
Yongji Wang, Huazhong Univ. of Science and Technology (China)
Qing Wu, Huazhong Univ. of Science and Technology (China)
Hong Wang, Univ. of Manchester Institute of Science and Technology (United Kingdom)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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