PREDICTION SYSTEM OF BURNING THROUGH POINT (BTP) BASED ON ADAPTIVE PATTERN CLUSTERING AND FEATURE MAP

摘要：

In this paper, due to the property of the long time delay,time varying and multimode of sintering process, a adaptive pattern clustering and feature map (APCFM) is proposed to solve the challenging problem for building a predictive system of burning through point. By using the density clustering and learning vector quantization (LVQ), the whole vector is divided automatically into subclasses which have similar clustering center and labeled fitting number, then these labeled subclasses samples are token into genetic neural network (GNN) to train. The training set consists 707 groups of actual process data and GNN are trained with APCFM algorithm, these experiments proved that this system is stronger robust and generality in clustering analysis and feature extraction.

关键词： Burning through point (BTP) pattern clustering feature map APCFM GNN

作者: WU-SHAN CHENG

作者单位: Computing Center, Department of Intelligent Robotics, Shanghai University of Engineering Science, Shanghai, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3089-3094

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）