Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Controllable-domain-based fuzzy rule extraction for copper removal process control|
|Citation:||IEEE Transactions on Fuzzy Systems, 2018; 26(3):1744-1756|
|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|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.