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

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