SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1925-1934.DOI: 10.1016/j.cjche.2015.11.012

• 第25届中国过程控制会议专栏 •    下一篇

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

Weiming Shao, Xuemin Tian, PingWang   

  1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China
  • 收稿日期:2015-05-19 修回日期:2015-07-15 出版日期:2015-12-28 发布日期:2016-01-19
  • 通讯作者: Xuemin Tian
  • 基金资助:

    Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

Weiming Shao, Xuemin Tian, PingWang   

  1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China
  • Received:2015-05-19 Revised:2015-07-15 Online:2015-12-28 Published:2016-01-19
  • Contact: Xuemin Tian
  • Supported by:

    Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

摘要: In soft sensor field, just-in-time learning (JITL) is an effective approach tomodel nonlinear and time varying processes. However,most similarity criterions in JITL are computed in the input space onlywhile ignoring important output information,whichmay lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problemcalled supervised local and non-local structure preserving projections (SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection, which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

关键词: Adaptive soft sensor, Just-in-time learning, Supervised local and non-local structure, preserving projections, Locality preserving projections, Database monitoring

Abstract: In soft sensor field, just-in-time learning (JITL) is an effective approach tomodel nonlinear and time varying processes. However,most similarity criterions in JITL are computed in the input space onlywhile ignoring important output information,whichmay lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problemcalled supervised local and non-local structure preserving projections (SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection, which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

Key words: Adaptive soft sensor, Just-in-time learning, Supervised local and non-local structure, preserving projections, Locality preserving projections, Database monitoring