摘要
提出了一种用于预转报炉炼钢终点磷含量的智能方法,在该方法中,采用RBFNN方法,对转炉冶炼过程进行建模与仿真。MATLAB中的神经网络工具箱是进行神经网络系统分析与设计的有力工具。RBF神经网络以其计算量小,学习速度快,不易陷入局部极小等诸多优点为系统辨识与建模提供了一种有效的手段。将二者结合起来,解决转炉冶炼中的建模问题,取得了令人满意的结果。
An intelligent method for predicting final P content of BOF is proposed. In the method, RBFNN is used to model and simulate the metallurgical process in BOF. MATLAB neural network toolbox is a powerful tool for system analyzing and designing of neural network. RBF neural network provides an effective means for system identification and modeling with its advantages of smaller calculation quantity and high learning speed. A combination of the two solved the problem of model building for BOF smelting, which has brought good results.
出处
《安徽冶金》
2009年第4期58-60,共3页
Anhui Metallurgy
