ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis

With the advancement of soft measurement, just-in-time learning (JITL) has become a widely adopted framework for online soft-sensing modeling in industrial processes. However, traditional JITL model approaches often rely on simple similarity measures like Euclidean distance, resulting in the underut...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2024-03, Vol.28 (2), p.284-295
Main Authors: Peng, Jian, Zhao, Liangcheng, Gao, Yilun, Yang, Jianjun
Format: Article
Language:English
Subjects:
Algorithms
Carbon
Discriminant analysis
Euclidean geometry
Measurement methods
Root-mean-square errors
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Summary:With the advancement of soft measurement, just-in-time learning (JITL) has become a widely adopted framework for online soft-sensing modeling in industrial processes. However, traditional JITL model approaches often rely on simple similarity measures like Euclidean distance, resulting in the underutilization of labeled data. This paper proposes a supervised, improved local Fisher discriminant analysis method based on a JITL framework and local Fisher discriminant analysis (LFDA) to improve data utilization efficiency. In particular, by incorporating the indirect correlation information matrix, this method integrates the inter-class and intra-class dispersion matrix, overcoming the limitation of the LFDA algorithm that only captures direct data correlations. We select two different carbon depositions in the Methanol-to-Olefin reaction system for comparative experiments and use the root mean squared error (RMSE) and R-square ( R 2 ) to evaluate the effectiveness of the proposed method. Fitting results show that two kinds of carbon depositions were better than the control model, namely the RMSE of the model were 0.1431 and 0.1513, R 2 were 0.8952 and 0.8707.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2024.p0284