淬火炉智能温度控制系统.docx

万方数据 Intelligent temperature control system of quench furnace作者：胡燕瑜， 桂卫华， 唐朝晖， 唐玲作者单位：胡燕瑜,桂卫华,唐朝晖(School of Information Science and Engineering,Central southUniversity,Changsha 410083,China)， 唐玲(Institution of Produce CommodityQuality Supervision and Testing,Changsha 410087,China)刊名：中国有色金属学会会刊(英文版)英文刊名：TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA年，卷(期)：2004，14(4)参考文献(14条) 1.LIU Gui-qing.Hakamada H Model analysis for combustion characteristics of RDF pellet 2002(03) 2.ZHEN Qiang.BAO Hong.WANG Fu-ming Prediction of thermodynamic properties for multicomponent system with Chou model期刊论文-Transactions of Nonferrous Metals Society of China 2000(05)3.CHENG Xiao ming Research on subsection temperature control system 1998( zk )4.WU Shi-qian.Joo Er M.YANG Gao A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks 2001(04)5.Cheong F.Lai R Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm 2000(01)6.Wang Jing.LI Yu-lan.CAI Zi-xin Optimal design method of fuzzy system based on gas 1999(05)7.Zhang Yun.MAO Zhong-yuan A generalized fuzzy-ANN networks and its learning algorithm 1998(01)8.Wang Yin.RONG Gang A self-organizing neuralnetwork-based fuzzy system 1999(06)9.Wang Yao-nan.ZHANG Chang-fan Genetic-based neurofuzzy network control for complicated industrial process 1999(06)10.Zhang Yi.LI Ren-hou Design of multi-variance fuzzy controller based on gas 1996(05)11.YAO Yi-wu.XIONG Jing-tao.MAO Zhong-yuan Self-organized fuzzy controller 1996(06)12.CHEN Jian-qin.CHEN Lai-jiu Learning of fuzzy rules based on self-adaptive neurons 1994(05)13.Santos M.Dexter A L Temperature control in liquid helium cryostat using self-learning neurofuzzy controller 2001(03)14.Sette S.Boullart L An implementation of genetic algorithms for rule based machine learning 2000 相似文献(2条) 1.外文期刊 HU Yan-yu.GUI Wei-hua.TANG Zhao-hui Intelligent temperature control system of quench furnace 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 deg C ; in stable stage, the scope of temperature change is controlled within + - 2 deg C , which meets the need of control veracity of temperature.2.外文会议 Shuqing Wang.Hui Liu.Zipeng Zhang.Suyi Liu Research on the Intelligent Control StrategyBased on FNNC and GAs for Hydraulic Turbine Generating Units It is difficult to gain better control performance using general control strategy to control Hydraulic turbine generating units system because it is a complicated non-linear MIMO system. In this study, a new control technique, which efficiently get optimal control parameters for fuzzy neural network controller through the training of neural network and Genetic algorithms, was proposed and then applied to control turbine generating unit system. In the designed control system, fuzzy reasoning rules, member function and parameters can be given through genetic algorithms when error is bigger and can be trained on-line through neural network when error is less. The improved genetic algorithms, which overcomes general genetic algorithms disadvantage, has quick training speed and gives whole optimized parameters for fuzzy neural network controller. RBF neural network is employed to identify and predict the relation between input and output of hydroelectric generating units system. Simulation experiment results show that the designed controller can control hydraulic turbine generating units efficaciously and has quick controlling speed and less controlling maxerror. So it provides a good control strategy for hydraulic turbine generating unit