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Proceedings Paper

Application of a neural network predictive control based on GGAP-RBF for the supercritical main steam
Author(s): Yun-Juan Li; Yan-jun Fang; Qi Li
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Paper Abstract

The Supercritical Main Steam has a large inertia, delay and nonlinear and dynamic characteristics change with the operating conditions, it is difficult to establish the precise mathematical model, this algorithm based on RBF neural network GGAP posed a direct neural network predictive controller, the combination of online learning and control to a supercritical power plant main stream temperature as the research object, MATLAB simulation results show that the superheated steam temperature system can achieve effective control, performance than the conventional PID control has greatly improved.

Paper Details

Date Published: 13 January 2012
PDF: 7 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83491Z (13 January 2012); doi: 10.1117/12.921376
Show Author Affiliations
Yun-Juan Li, Kunming Univ. (China)
Wuhan Univ. (China)
Yan-jun Fang, Naval Univ. of Engineering (China)
Qi Li, Naval Univ. of Engineering (China)


Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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