Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113068
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Type: Journal article
Title: Controllable-domain-based fuzzy rule extraction for copper removal process control
Author: Zhang, B.
Yang, C.
Zhu, H.
Shi, P.
Gui, W.
Citation: IEEE Transactions on Fuzzy Systems, 2018; 26(3):1744-1756
Publisher: IEEE
Issue Date: 2018
ISSN: 1063-6706
1941-0034
Statement of
Responsibility: 
Bin Zhang, Chunhua Yang, Hongqiu Zhu, Peng Shi, and Weihua Gui
Abstract: In copper removal process control, the commonly used technique is the so-called rule-based control, which is largely dependent upon the operators’ experience, likely leading to unstable process production due to each individual’s characters and favors. In this paper, to enhance the effectiveness of process control, a controllable-domain-based fuzzy rule extraction strategy is proposed. New definitions of representative controlled samples are introduced, by which the input variable space is divided into several controllable domains by applying positive and unlabeled learning algorithm. Also, the unreasonable removed and the controllable domains are accordingly determined. Then, support vector machine method is employed to extract fuzzy control rules for different domains. Finally, an industrial experiment is presented to demonstrate the effectiveness and advantages of the developed new design scheme.
Keywords: Copper removal; fuzzy logic; positive and unlabeled learning (PU learning); rule extraction; support vector machine (SVM)
Description: Date of publication September 18, 2017; date of current version May 31, 2018.
Rights: © 2017 IEEE
RMID: 0030076873
DOI: 10.1109/TFUZZ.2017.2751000
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
http://purl.org/au-research/grants/arc/LP140100471
http://purl.org/au-research/grants/arc/LE150100079
Appears in Collections:Electrical and Electronic Engineering publications

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