Research on a Sample Learning Model Based on SVR-GA Hybrid Algorithm

摘要：

with the rapid development of industry,a lot of companies demand a high degree of material plate flatness.With the leveler in a certain machine structure,the selecting of levelers technical parameters directly decides the plate flatness after leveling.In this paper,through the analyses of samples features in the work of leveling and the study of related theories,combined with the requirements of its work at the scene,designs a sample learning model based on SVR-GA hybrid algorithm which completes two major tasks of knowledge acquisition and technical parameters selecting.The algorithm has been used to predict the flatness of the plate in the modeling industry and the result shows that the algorithm not only improves the Prediction accuracy but also has the ability to update in real-time online environment

关键词： support vector machine regression genetic algorithm batch increment online sample learning

作者: Xu Hong-zhe Chao Lu-meng Chen Ming

作者单位: Xian Jiao tong University associate professor Xian,China Xian Jiao tong University postgraduate Xian Jiao tong University

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 476-480

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）