计算机工程与应用2011，Vol.47Issue(21)：235-238,4.DOI:10.3778/j.issn.1002-8331.2011.21.062
局部KPLS特征提取的LSSVM软测量建模方法
Soft sensor modeling based on local KPLS feature extraction and on-line LSSVM
摘要
Abstract
To deal with complex industrial process variables with strong correlation,non-linearity and time-varying characteristics of operation condition, a new soft sensor modeling method is proposed based on local Kernel Partial Least Squares (KPLS) feature extraction and on-line LSSVM.Some similar samples are found out with the current test sample from the whole sample space, and features of the subspace are extracted, and then a local soft sensor model based on LSSVM is built to estimate the current output.Experimental results show that this method can effectively realize feature extraction, and have a better generalization ability than off-line LSSVM based on global feature extraction with KPLS as well as global LSSVM without feature extraction.关键词
核偏最小二乘/在线最小二乘支持向量机(LSSVM)/局部学习/特征提取Key words
Kernel Partial Least Squares(KPLS)/on-line Least Squares Support Vector Machines(LSSVM)/local learning/feature extraction分类
信息技术与安全科学引用本文复制引用
李雅芹,杨慧中..局部KPLS特征提取的LSSVM软测量建模方法[J].计算机工程与应用,2011,47(21):235-238,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60674092) （the National Natural Science Foundation of China under Grant No.60674092）
江.苏省高技术研究项目(工业部分)(No.BG2006010) （工业部分）
江南大学创新团队发展计划资助项目. （）
