Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

Deng, Z; Jiang, Y; Cao, L; Wang, S

Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

Deng, Z Jiang, Y Cao, L

Wang, S

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2014, pp. 178 - 185
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Deng, Z	en_US
dc.contributor.author	Jiang, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Wang, S	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2014, pp. 178 - 185	en_US
dc.identifier.isbn	9781479920723	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121603
dc.description.abstract	© 2014 IEEE. In this study, the improved knowledge-leverage based TSK fuzzy system modeling method is proposed in order to overcome the weaknesses of the knowledge-leverage based TSK fuzzy system (TSK-FS) modeling method. In particular, two improved knowledge-leverage strategies have been introduced for the parameter learning of the antecedents and consequents of the TSK-FS constructed in the current scene by transfer learning from the reference scene, respectively. With the improved knowledge-leverage learning abilities, the proposed method has shown the more adaptive modeling effect compared with traditional TSK fuzzy modeling methods and some related methods on the synthetic and real world datasets.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2014.6891544	en_US
dc.title	Knowledge-leverage based TSK fuzzy system with improved knowledge transfer	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. In this study, the improved knowledge-leverage based TSK fuzzy system modeling method is proposed in order to overcome the weaknesses of the knowledge-leverage based TSK fuzzy system (TSK-FS) modeling method. In particular, two improved knowledge-leverage strategies have been introduced for the parameter learning of the antecedents and consequents of the TSK-FS constructed in the current scene by transfer learning from the reference scene, respectively. With the improved knowledge-leverage learning abilities, the proposed method has shown the more adaptive modeling effect compared with traditional TSK fuzzy modeling methods and some related methods on the synthetic and real world datasets.

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http://hdl.handle.net/10453/121603