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《Journal of Chongqing University》 2009-01
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A radial basis function neural network based self-adapting predictive decoupling control system for gas collector pressure in coke ovens

ZHANG Shi-feng,ZHOU Jian-fang(Department of Electrical Engineering& Information,Anhui University of Technology,Ma'anshan 243002,P.R.China)  
Pressure is measured at different levels in the loop layer because self-adapting predictive decoupling control systems are strongly coupled,disturbed,and non-linear and there is a long time delay for gas collector pressure systems in coke ovens.By combing the traditional neural network control and proportional integral differential(PID) controllers based on radial basis function(RBF) neural network identification,the gas collector pressure is ensured to reach the desired technology range.The prediction model of an RBF neural network is used for advanced prediction of the actual output pressure to overcome delays in general gas collection.The simulation results and application indicate that the method can obtain ideal control results.
【Fund】: 安徽省教育厅自然科学研究资助项目(KJ2008B104)
【CateGory Index】: TP273
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