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JACIII Vol.28 No.2 pp. 284-295
doi: 10.20965/jaciii.2024.p0284
(2024)

Research Paper:

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

Jian Peng*,**,***, Liangcheng Zhao*,**,***,†, Yilun Gao*,**,***, and Jianjun Yang*,**,***

*School of Automation, China University of Geosciences
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

Received:
March 16, 2023
Accepted:
October 20, 2023
Published:
March 20, 2024
Keywords:
soft measurement, JITL, similarity measure, ILFDA
Abstract

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 (R2) 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, R2 were 0.8952 and 0.8707.

Cite this article as:
J. Peng, L. Zhao, Y. Gao, and J. Yang, “ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 284-295, 2024.
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