基于模糊聚类及神经网络的连铸漏钢预报

在模糊RBF神经网络的基础上，通过融合基于贴近度的改进FCM和Conditional FCM算法，建立了改进的模糊RBF网络模型；并结合某钢厂连铸现场采集的历史数据将该模型应用于连铸漏钢预报的过程中。结果表明，改进的网络模型能更准确地辨识连铸粘结漏钢过程中典型温度模式1和模式2，对二者的预报率分别达到949%和983%，报出率均达到100%，其预报性能更佳，能更有效地预报拉漏事故。

Breakout Prediction Based on Fuzzy Clustering and Fuzzy Neural Network in Continuous Casting Process

Based on fuzzy RBF neural network, an improved fuzzy RBF network model by syncretizing closeness based FCM and conditional FCM fuzzy clustering algorithm was presented. With history data acquired in a steel work, the model was applied to the breakout prediction of continuous casting process. The results show that the prediction rates of the model for two typical temperature patterns of sticking breakout were 94.9% and 98.3% respectively, and both of the quote rates were 100%, that indicates the model is more effective in indentifying these two typical temperature patterns and predicting possible leakages of liquid steel.