中國(guó)有色金屬學(xué)報(bào)(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 14 No. 4 August 2004 |
TANG Zhao-hui(唐朝暉)1, TANG Ling(唐 玲)2
（1.School of Information Science and Engineering,
Central south University, Changsha 410083, China;
2.Institution of Produce Commodity Quality Supervision and Testing, Changsha 410087, China）
Abstract:A fuzzy-neural networks intelligent temperature control system of quench furnace was presented. Combined genetic algorithm with back-propagation algorithm, the weight values of neural networks, parameters of fuzzy membership functions and inference rules can be adjusted automatically, which realizes the optimal control of temperature. The results show that this control system can run effectively with satisfied temperature precision: in temperature uprising stage,
overshot of temperature is under 4℃; in stable stage, the scope of temperature change is controlled within±2℃, which meets the need of control veracity of temperature.
Key words: fuzzy-neural networks; MIMO system; genetic algorithm
