An interval type-2 neural fuzzy system for online system identification and feature elimination

Lin, CT; Pal, NR; Wu, SL; Liu, YT; Lin, YY

An interval type-2 neural fuzzy system for online system identification and feature elimination

Lin, CT

Pal, NR Wu, SL Liu, YT

Lin, YY

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (7), pp. 1442 - 1455
Issue Date:: 2015-07-01

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Pal, NR	en_US
dc.contributor.author	Wu, SL	en_US
dc.contributor.author	Liu, YT https://orcid.org/0000-0001-6184-2373	en_US
dc.contributor.author	Lin, YY	en_US
dc.date.issued	2015-07-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (7), pp. 1442 - 1455	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116466
dc.description.abstract	© 2012 IEEE. We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2014.2346537	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Normal Distribution	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Chemical Industry	en_US
dc.subject.mesh	Automobiles	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Online Systems	en_US
dc.subject.mesh	Machine Learning	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	An interval type-2 neural fuzzy system for online system identification and feature elimination	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	26	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2012 IEEE. We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

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http://hdl.handle.net/10453/116466