Controllable-Domain-Based Fuzzy Rule Extraction for Copper Removal Process Control

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.

[1]  Wanli Zuo,et al.  Reliable Negative Extracting Based on kNN for Learning from Positive and Unlabeled Examples , 2009, J. Comput..

[2]  M. N. Nguyen,et al.  pro-Positive Unlabeled Learning for Time Series Classification , 2022 .

[3]  Francisco Herrera,et al.  Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between $n$-Dimensional Overlap Functions and Decomposition Strategies , 2015, IEEE Transactions on Fuzzy Systems.

[4]  Changchun Hua,et al.  Decentralized Networked Control System Design Using T–S Fuzzy Approach , 2012, IEEE Transactions on Fuzzy Systems.

[5]  Jesús Alcalá-Fdez,et al.  A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection , 2007, IEEE Transactions on Fuzzy Systems.

[6]  Ronald R. Yager,et al.  Perception-based granular probabilities in risk modeling and decision making , 2006, IEEE Transactions on Fuzzy Systems.

[7]  A. J. Yuste,et al.  Knowledge Acquisition in Fuzzy-Rule-Based Systems With Particle-Swarm Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[8]  Nikhil R. Pal,et al.  An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification , 2012, IEEE Transactions on Fuzzy Systems.

[9]  Andrew P. Bradley,et al.  Rule extraction from support vector machines: A review , 2010, Neurocomputing.

[10]  Ying Han,et al.  Bipolar-Valued Rough Fuzzy Set and Its Applications to the Decision Information System , 2015, IEEE Transactions on Fuzzy Systems.

[11]  María José del Jesús,et al.  Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department , 2011, Soft Comput..

[12]  Johan A. K. Suykens,et al.  A robust ensemble approach to learn from positive and unlabeled data using SVM base models , 2014, Neurocomputing.

[13]  Weihua Gui,et al.  Evaluation strategy for the control of the copper removal process based on oxidation–reduction potential , 2016 .

[14]  Yuxin Zhao,et al.  Interval Type-2 Fuzzy Model Predictive Control of Nonlinear Networked Control Systems , 2015, IEEE Transactions on Fuzzy Systems.

[15]  Weihua Gui,et al.  Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes , 2016 .

[16]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[17]  Yuan-Hai Shao,et al.  Laplacian unit-hyperplane learning from positive and unlabeled examples , 2015, Inf. Sci..

[18]  Lei Xi,et al.  Rough set and ensemble learning based semi-supervised algorithm for text classification , 2011, Expert Syst. Appl..

[19]  Kemal Polat,et al.  A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems , 2009, Expert Syst. Appl..

[20]  See-Kiong Ng,et al.  Negative Training Data Can be Harmful to Text Classification , 2010, EMNLP.

[21]  Muttukrishnan Rajarajan,et al.  Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud , 2014, IEEE Transactions on Dependable and Secure Computing.

[22]  Ashkan Sami,et al.  Semi-supervised outlier detection with only positive and unlabeled data based on fuzzy clustering , 2013, The 5th Conference on Information and Knowledge Technology.

[23]  Edwin Lughofer,et al.  SparseFIS: Data-Driven Learning of Fuzzy Systems With Sparsity Constraints , 2010, IEEE Transactions on Fuzzy Systems.

[24]  José Manuel Benítez,et al.  On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems , 2016, Inf. Sci..

[25]  Wenkai Li,et al.  A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Junfei Qiao,et al.  Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process , 2014, IEEE Transactions on Industrial Electronics.

[27]  Chuanhou Gao,et al.  Rule Extraction From Fuzzy-Based Blast Furnace SVM Multiclassifier for Decision-Making , 2014, IEEE Transactions on Fuzzy Systems.

[28]  Weihua Gui,et al.  An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential , 2013 .

[29]  Antonio F. Gómez-Skarmeta,et al.  A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling , 1997, IEEE Trans. Fuzzy Syst..

[30]  Xiaoli Li,et al.  Learning to Classify Texts Using Positive and Unlabeled Data , 2003, IJCAI.

[31]  Kevin Chen-Chuan Chang,et al.  PEBL: positive example based learning for Web page classification using SVM , 2002, KDD.

[32]  David P. Pancho,et al.  FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility , 2013, IEEE Transactions on Fuzzy Systems.

[33]  Peng Shi,et al.  Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty , 2012, Int. J. Syst. Sci..

[34]  Philip S. Yu,et al.  Partially Supervised Classification of Text Documents , 2002, ICML.

[35]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[36]  Kok Lay Teo,et al.  Optimal control for zinc solution purification based on interacting CSTR models , 2012 .

[37]  Wanli Zuo,et al.  Learning from Positive and Unlabeled Examples: A Survey , 2008, 2008 International Symposiums on Information Processing.

[38]  Ruggero G. Pensa,et al.  Positive and unlabeled learning in categorical data , 2016, Neurocomputing.

[39]  Wassim M. Haddad,et al.  Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems , 2012, IEEE Transactions on Control Systems Technology.

[40]  Hamideh Afsarmanesh,et al.  Semi-supervised self-training for decision tree classifiers , 2017, Int. J. Mach. Learn. Cybern..

[41]  Jose Jesus Castro-Schez,et al.  Knowledge acquisition based on learning of maximal structure fuzzy rules , 2013, Knowl. Based Syst..

[42]  Chunhua Yang,et al.  Intelligent optimal setting control of a cobalt removal process , 2014 .

[43]  Lale Özbakir,et al.  Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks , 2013, Expert Syst. Appl..

[44]  Weihua Gui,et al.  Kinetic Modeling and Parameter Estimation for Competing Reactions in Copper Removal Process from Zinc Sulfate Solution , 2013 .

[45]  Mei Li,et al.  Classifying networked text data with positive and unlabeled examples , 2016, Pattern Recognit. Lett..

[46]  Francisco Herrera,et al.  Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions , 2011, Soft Comput..

[47]  Francisco Herrera,et al.  A Fast and Scalable Multiobjective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems , 2011, IEEE Transactions on Fuzzy Systems.