Adaptive multi-LSSVR based soft sensing for cobalt oxalate synthesis process
An adaptive multiple least squares support vector regression (multi-LSSVR) method to enhance the model prediction accuracy and generalization capability is presented. In the proposed approach, data for building single LSSVR models is re-sampled based on bootstrap techniques to form a number of sets of training and test data. For each data set, a LSSVR model is developed which are then aggregated through partial least squares (PLS). In order to identify the changes of process, an ef.ciently adaptive strategy based on batch-to-batch information is used. It ef.ciently updates a trained multi-LSSVR model by means of incremental updating and decremental pruning algorithms whenever a new batch sample is added to, or removed from the training set. The proposed method is demonstrated on a simulated batch process and utilized to develop a soft sensor model for cobalt oxalate synthesis process of hydrometallurgy. It is shown that the method can not only enhance model prediction accuracy, but also track the system drift by compared against single LSSVR method.
cobalt oxalate synthesis process Least squares support vector regression soft sensor Batch processes
Shuning Zhang Fuli Wang Dakuo He Runda Jia
Ph.D. candidates of School of Information Science and Engineering, Northeastern University, 110004 S School of Information Science and Engineering, Northeastern University and Key Laboratory of Integra
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
255-260
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）