kNN-RVM Lazy Learning Approach for Soft-sensing Modeling of Fed-Batch processes

摘要：

Fed-batch processes are inherently difficult to model owing to non-steady-state operation, small-sample condition, instinct time-variation and batch-to-batch variation caused by drifting. Furthermore, when the process switches to different operation phrases, global learning modeling methods would suffer poor performance due to the negative impact of overdue training samples. In this paper, a k nearest neighbor relevance vector machine (kNN-RVM) based lazy learning method is proposed to model the fed-batch processes to soft-sense the corresponding production indices. A recursive algorithm is developed to effectively obtain the kernel matrices used by previous kNN step and following modeling process. Simulative soft-sensors of penicillin production process and rubber mixing process are implemented to valid the proposed method. Comparative results indict that proposed method has better precision and much lower computational complexity than relevance vector machine (RVM) on soft-sensing modeling of fed-batch processes.

作者: Jun Ji Hai-qing Wang Kun Chen Dian-cai Yang

作者单位: State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control,Zheji National Tire Process and Control Engineering Research Center, Qingdao, China

会议类型: 国际会议

会议名称: 2011 International Symposium on Advanced Control of Industrial Processes(2011工业过程先进控制技术国际研讨会)

会议地点: 杭州

会议语种:英文

页码: 272-276

在线出版日期: 2011-05-01（万方平台首次上网日期，不代表论文的发表时间）