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|Title:||A New Strategy of Locality Enhancement for Justin-Time Learning Method||Authors:||Su, Q.L.
|Issue Date:||2012||Citation:||Su, Q.L.,Kano, M.,Chiu, M.-S. (2012). A New Strategy of Locality Enhancement for Justin-Time Learning Method. Computer Aided Chemical Engineering 31 : 1662-1666. ScholarBank@NUS Repository. https://doi.org/10.1016/B978-0-444-59506-5.50163-2||Abstract:||Just-in-Time Learning (JITL) method has recently received increasing attention, particularly its application to control and soft sensing. Unlike the conventional JITL methods, which construct a local model directly based on the relevant data selected from reference database, a novel strategy is proposed by considering the local model as a Taylor series of the global model expanded in the vicinity of a reference point. This reference point could be chosen as the most relevant data or the query data. A comparative study using a benchmark nonlinear CSTR process showed the efficiency of the proposed strategy by achieving better prediction than its conventional counterparts. © 2012 Elsevier B.V.||Source Title:||Computer Aided Chemical Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/54545||ISSN:||15707946||DOI:||10.1016/B978-0-444-59506-5.50163-2|
|Appears in Collections:||Staff Publications|
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