中国机械工程2016，Vol.27Issue(12)：1609-1614,6.DOI:10.3969/j.issn.1004-132X.2016.12.009
基于主动学习GA-SVM分类器的连铸漏钢预报
Breakout Prediction Classifier for Continuous Casting Based on Active Learning GA-SVM
摘要
Abstract
Aiming at the problem that was difficult to obtain a high accurate breakout prediction model of continuous casting in the case of small sample data,a breakout prediction algorithm was pro—posed based on active learning GA-SVM classifier.Firstly,the algorithm preprocessed temperature data of continuous casting mold and labels valid data.Secondly,SVM model was obtained after SVM empirical parameters were optimized using labeled small sample data and GA.Finally,the optimized SVM model was tested using the historical data of a steel plant.The results show that in the case of small sample data for training model,the breakout prediction algorithm based on active learning GA-SVM classifier can obtain higher breakout prediction accuracy and 100% reported ratio.The presented breakout steel prediction algorithm was validated.关键词
漏钢预报/GA-SVM/主动学习/小样本数据Key words
breakout prediction/genetic algorithm-support vector machine (GA-SVM)/active learning/small sample data分类
矿业与冶金引用本文复制引用
方一鸣,胡春洋,刘乐,张兴明..基于主动学习GA-SVM分类器的连铸漏钢预报[J].中国机械工程,2016,27(12):1609-1614,6.基金项目
国家自然科学基金委员会与宝钢集团有限公司联合资助项目(U1260203) （U1260203）
国家自然科学基金资助项目(61403332) （61403332）
河北省自然科学基金钢铁联合基金资助项目(F201320329) （F201320329）
河北省高等学校创新团队领军人才培育计划资助项目(LJRC013) （LJRC013）
