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Temperature control of annealing furnaces based on improved PSO and fuzzy RBF neural network


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Temperature control of annealing furnaces based on improved PSO and fuzzy RBF neural network
Li JiejiaLi XiaofengPian Jinxiang
Information and Control Engineering Faculty,Shenyang Jianzhu University,Shenyang 110168,China
improved particle swarm optimization algorithmfuzzy radial basis function neural networkannealing furnacestemperature controlradial basis functionweightsthresholdsovershootresponse timesteady state errors
In order to improve the control accuracy of temperature control systems of annealing furnaces with large time delay and strong coupling,the temperature of annealing furnaces is controlled by a fuzzy radial basis function(RBF)neural network and optimized by an improved particle swarm optimization(PSO)algorithm.The system functions are unified using the function equivalency of the fuzzy inference process and RBF neural network.The initial weights and thresholds of the fuzzy RBF neural network are obtained by the PSO algorithm,and the final weights and thresholds are obtained by quadratic optimization when the fuzzy RBF neural network is trained by the improved PSO algorithm.The simulation results show that the method proposed here decreases the overshoot,shortens the response time,and the steady state error is small,which can fit the outputs of the reference model and is better than common PID control in control effects.


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Last Update: 2014-06-30