【摘要】 针对武汉钢铁集团公司大型轧钢厂当前在高速线材生产线中存在的水冷控制系统可靠性差,轧线温度波动范围大等问题,应用智能计算理论及方法对上述工业控制系统进行系统辨识、建模以及优化.分析比较了基于梯度下降搜索BP算法、径向基函数网络、Levenberg Marquardt BP算法的前馈神经网络对SMS水冷系统的逼近精度、训练速度.研究了采用Levenberg-Marquardt BP算法的前馈神经网络在样本集和测试集上的表现,建立了基于Levenberg-Marquardt BP算法的前馈神经网络水冷控制系统模型.解决了高速线材水冷控制系统可靠性,温度控制精度问题.更多还原

【Abstract】 In this work,we investigate the problems existing in high-speed wire production line of a large rolling mill of Wuhan Iron and Steel Group Corporation.Specifically,the following two problems are considered:the reliability of water control system is poor,the temperature of rolling line fluctuation is unstable.We apply the intelligent computing theories and methods to model and optimize the water control system.In order to improve the approximation precision and the training speed of SMS water cooling system,the gradient descent search BP algorithm,radial basis function network,and Levenberg-Marquardt BP algorithm are used.By using the Levenberg-Marquardt BP feedforward neural network which perform on the sample and test set,we construct a feedforward neural network water cooling control system model which is based on the Levenberg-Marquardt BP algorithm.The work has improved the reliability of the system and the precision of the temperature control.更多还原