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Proceedings Paper

Study on static and dynamic modeling of nonlinear system
Author(s): Liping Qu; Hongjian Wang; Xinqian Bian; Kejun Wang
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Paper Abstract

In this paper, a means, which is based on Radial Basis Function Neural Network (RBFNN), is firstly presented to make the model for nonlinear systems. As an application example of this means, the high power DC graphitizing furnace is analyzed, and the RBF model for graphitizing furnace is constructed from experiments or simulations. The procedures for training the model are described along with discussions on error. Secondly, the open loop dynamic model is discussed in detail. The dynamic model of graphitizing furnace is accomplished in this paper. All the simulated results show that the discussed approaches are effective.

Paper Details

Date Published: 13 October 2008
PDF: 7 pages
Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271S (13 October 2008); doi: 10.1117/12.806446
Show Author Affiliations
Liping Qu, Harbin Univ. of Engineering (China)
BeiHua Univ. (China)
Hongjian Wang, Harbin Univ. of Engineering (China)
Xinqian Bian, Harbin Univ. of Engineering (China)
Kejun Wang, Harbin Univ. of Engineering (China)


Published in SPIE Proceedings Vol. 7127:
Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence

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