Nonlinear dynamic system modeling based on neural state space model

Yongji Wang; Qing Wu; Hong Wang

doi:10.1117/12.441671

20 September 2001 Nonlinear dynamic system modeling based on neural state space model

Yongji Wang, Qing Wu, Hong Wang

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441671
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Yongji Wang, Qing Wu, and Hong Wang "Nonlinear dynamic system modeling based on neural state space model", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441671

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