南京理工大学学报（自然科学版）Issue(3)：337-341,5.
基于改进 PSO 和模糊 RBF 神经网络的退火炉温控制
Temperature control of annealing furnaces based on improved PSO and fuzzy RBF neural network
摘要
Abstract
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 e-quivalency 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.关键词
改进粒子群优化算法/模糊径向基函数神经网络/退火炉/温度控制/径向基函数/权值/阀值/超调量/响应时间/稳态误差Key words
improved particle swarm optimization algorithm/fuzzy radial basis function neural network/annealing furnaces/temperature control/radial basis function/weights/thresholds/overshoot/response time/steady state errors分类
信息技术与安全科学引用本文复制引用
李界家,李晓峰,片锦香..基于改进 PSO 和模糊 RBF 神经网络的退火炉温控制[J].南京理工大学学报（自然科学版）,2014,(3):337-341,5.基金项目
国家自然科学基金(60874103) （60874103）
