[1]QI Cheng,SHI Xudong,XIONG Weili.A just-in-time learning soft sensor modeling method based on the second-order similarity[J].CAAI Transactions on Intelligent Systems,2020,15(5):910-918.[doi:10.11992/tis.201809040]
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A just-in-time learning soft sensor modeling method based on the second-order similarity

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 910-918 Column: 学术论文—机器学习 Public date: 2020-09-05
Title:
A just-in-time learning soft sensor modeling method based on the second-order similarity
Author(s):
QI Cheng1;  SHI Xudong1;  XIONG Weili1; 2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry Jiangnan University, Ministry of Education, Wuxi 214122, China
Keywords:
just-in-time learning; update frequency; second-order similarity; similarity criterion; first-order similarity; local model; cumulative similarity factor; similarity threshold
CLC:
TP273
DOI:
10.11992/tis.201809040
Abstract:
Aiming at the indirect accuracy reduction caused by the frequency reduction of just-in-time (lazy) learning model, a second-order similarity just-in-time learning method is proposed. This method takes into account the overall distribution characteristics of the sample set, establishes a second-order similarity criterion based on the traditional first-order similarity criterion, and uses a second-order similarity sample with most of the same neighbors as the test sample to establish the model at the current time. At the same time, the cumulative similarity factor is used to determine the sample size when the local model is established, and the similarity threshold is used to determine whether the model needs to be rebuilt at this time. This method has been effectively validated in the prediction experiment of the product concentration in the fermentation process of penicillin.
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