摘要
以某钢厂引进的连铸板坯二冷动态控制系统为研究对象,提出了基于改进粒子群BP算法的板坯二冷区表面温度神经网络控制器。以实际生产现场的设备、工艺参数为基础进行了仿真研究,结果表明表面温度神经网络控制器的输出结果与实际生产参数的误差小于2%,研究结果对引进的同类连铸板坯二冷控制系统的升级改造具有指导意义。
Surface temeperature neural network controller （TNNC） is suggested that based on improved particle swarm optimization back propagation （IPSOBP） algorithm about introduced secondary cooling dynamic control system in casting slab of some steel mills. Simulation result on the parameter of quipment and realy technological producing indicates that the error between TNNC output result and reality one is smaller than 2%. The result has guiding significance to the promotion and reformation of same kind introduced control system.
出处
《冶金能源》
北大核心
2008年第1期19-22,共4页
Energy For Metallurgical Industry
关键词
连铸板坯
表面温度
控制器
粒子群算法
continuous slabs casting surface temeperature controller particle swarm algorithm
