An interval type-2 neural fuzzy inference system (IT2NFIS) with compensatory operator

Lin, YY; Chang, JY; Lin, CT

An interval type-2 neural fuzzy inference system (IT2NFIS) with compensatory operator

Lin, YY Chang, JY Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 2013, pp. 884 - 889
Issue Date:: 2013-10-31

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Field	Value	Language
dc.contributor.author	Lin, YY	en_US
dc.contributor.author	Chang, JY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-10-31	en_US
dc.identifier.citation	Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 2013, pp. 884 - 889	en_US
dc.identifier.isbn	9781479903474	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120929
dc.description.abstract	In this paper, an interval type-2 neural fuzzy system (IT2NFIS) with compensatory operator is proposed for system modeling. The IT2NFIS uses type-2 fuzzy sets in the premise clause in order to effectively handle the uncertainties in terms of data and information. The premise part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the IT2NFIS, where compensatory operation is able to adaptively adjust fuzzy membership functions and to dynamically optimize fuzzy operations. The consequent part in the IT2NFIS consists of the Takagi-Sugeno-Kang (TSK) type that is a linear combination of exogenous input variables. Initially the rule base in the IT2NFIS is empty. All rules generated are based on on-line type-2 fuzzy clustering. All free weights are learned by a gradient descent algorithm to improve the learning performance. Simulation results show that our approach yields smaller root mean squared errors than its rivals. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013	en_US
dc.relation.isbasedon	10.1109/IFSA-NAFIPS.2013.6608517	en_US
dc.title	An interval type-2 neural fuzzy inference system (IT2NFIS) with compensatory operator	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, an interval type-2 neural fuzzy system (IT2NFIS) with compensatory operator is proposed for system modeling. The IT2NFIS uses type-2 fuzzy sets in the premise clause in order to effectively handle the uncertainties in terms of data and information. The premise part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the IT2NFIS, where compensatory operation is able to adaptively adjust fuzzy membership functions and to dynamically optimize fuzzy operations. The consequent part in the IT2NFIS consists of the Takagi-Sugeno-Kang (TSK) type that is a linear combination of exogenous input variables. Initially the rule base in the IT2NFIS is empty. All rules generated are based on on-line type-2 fuzzy clustering. All free weights are learned by a gradient descent algorithm to improve the learning performance. Simulation results show that our approach yields smaller root mean squared errors than its rivals. © 2013 IEEE.

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http://hdl.handle.net/10453/120929