摘要
现有拉漏预报方法经常会发生误报，本文尝试了一种基于神经网络的漏钢预报方法，它采用神经网络进行模糊模式识别和预测拉漏事故．实验表明该方法能比原有方法更快速准确地检测出铸坯粘结和裂缝等缺陷，可有效预防连铸中的漏钢事故．
False alarms are often found in the mould monitoring system of continuous casting process,thus a steel leakage alarm method is suggested in this paper. Fuzzy neural networks are used to perform temperature pattern recognition and leakage alarm. Experiments show that it can detect sticking or splits of slab more quickly and is very effective in avoiding leakage of molten steel.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
1998年第4期593-598,共6页
Control Theory & Applications
基金
辽宁省优秀青年科研人才培养基金!963007
国家计委"九五"科技攻关项目!85-524-02-05
关键词
漏钢预报
模糊神经网络
连续铸钢
continuous casting
leakage alarm
fuzzy neural network
pattern recognition
