中国机械工程2017，Vol.28Issue(12)：1462-1467,6.DOI:10.3969/j.issn.1004-132X.2017.12.012
基于模拟退火粒子群算法优化支持向量机参数的连铸漏钢预报
Breakout Prediction for Continuous Casting Based on SA-PSO to Optimize Parameters of SVM
摘要
Abstract
In order to overcome the problems that the neural network model was difficult to obtain a high accurate breakout prediction for continuous casting under the conditions of small sample train-ing data,a breakout prediction for continuous casting was proposed based on SA-PSO algorithm to optimize the parameters of SVM.Firstly,the PSO algorithm was introduced into the training proces-ses of SVM,increasing the optimization speeds of breakout prediction model by using the advantages of less parameters and fast optimization speeds.Secondly,SA algorithm was used to evaluate the new positions of updated particles,and to determine whether the new positions were accepted,which could avoid the PSO algorithm steped into the local extremum in optimization processes.Finally,the break-out prediction for continuous casting was tested by the history data of continuous casting.The results show that the proposed algorithm may make the breakout prediction accuracy reach 98.8%.关键词
连铸漏钢预报/支持向量机/粒子群算法/模拟退火算法Key words
breakout prediction for continuous casting/support vector machine (SVM)/particle swarm optimization(PSO)algorithm/simulated annealing(SA)algorithm.分类
矿业与冶金引用本文复制引用
方一鸣,郑贺军,刘乐,胡春洋..基于模拟退火粒子群算法优化支持向量机参数的连铸漏钢预报[J].中国机械工程,2017,28(12):1462-1467,6.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）
河北省高等学校科学技术研究青年基金资助项目(QN2016122) （QN2016122）
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）
