Efficient quality variable prediction of industrial process via fuzzy neural network with lightweight structure

Jie Wang (), Shiwen Xie (), Yongfang Xie () and Xiaofang Chen ()
Additional contact information
Jie Wang: Central South University
Shiwen Xie: Central South University
Yongfang Xie: Central South University
Xiaofang Chen: Central South University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 26, 459-474

Abstract: Abstract Quality Variables of industrial processes generally require to be obtained as fast as possible. In this paper, a correlation-wise self-organizing fuzzy neural network (CwSFNN) for efficient quality variables prediction of industrial process is proposed. Firstly, the correlation-wise self-organizing mechanism is developed by calculating the correlations between quality variables and fuzzy rules to optimize the network structure. The fuzzy rules of CwSFNN are generated or pruned systematically during the learning process, which can both improve the modeling performance and decrease the computational complexity. Moreover, the loss performance and convergence of CwSFNN are theoretically analyzed to ensure its successful application in practice. The benchmark Tennessee Eastman process (TEP) and real-world aluminum electrolysis process are presented to verify the effectiveness of CwSFNN. The experimental results show that the proposed CwSFNN performs better performance in both quality variable prediction and computation cost compared with some advanced methods. The source code of proposed CwSFNN is available at https://github.com/wjiecsu/CwSFNN .

Keywords: Quality variable prediction; Fuzzy neural network; Correlation-wise self-organizing; Tennessee Eastman process; Aluminum electrolysis process (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02254-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02254-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02254-6

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().