舰船电子工程2018，Vol.38Issue(6)：26-29,57,5.DOI:10.3969/j.issn.1672-9730.2018.06.007
基于选择性核学习的在线软测量建模方法
Online Soft Sensing Method Based on Selective Kernel Learning
赵成斌 1陈鹏 1梁洁1
作者信息
- 1. 中国人民解放军91206部队 青岛 266108
- 折叠
摘要
Abstract
The performance of off-line build soft sensor often deteriorates,where updating the model online is necessary. For this reason,an approach based on selective kernel learning for on line soft sensing is proposed. This method,utilizing the least squares support vector machine(LSSVM)for constructing offline model,employs the strategy of prediction error bound(PEB)to carry out forward learning selectively so as to enhance model sparsity. Moreover,in order to delete redundant samples more accurate?ly in backward learning,this paper proposes a similarity criterion in high dimensional feature space which incorporates the input and output information simultaneously so that the most dissimilar sample to the current state is selected and eliminated. The forego?ing scheme is applied to build the soft sensor of melt index of polypropylene and the result has demonstrated the effectiveness of the proposed method.关键词
软测量/选择性核学习/最小二乘支持向量机/相似度Key words
soft sensing/selective kernel learning/least squares support vector machine/similarity分类
信息技术与安全科学引用本文复制引用
赵成斌,陈鹏,梁洁..基于选择性核学习的在线软测量建模方法[J].舰船电子工程,2018,38(6):26-29,57,5.
