On the learning of min-max fuzzy systems

Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/81632

DC Field	Value
dc.title	On the learning of min-max fuzzy systems
dc.contributor.author	Tan, Shaohua
dc.contributor.author	Zhang, Li
dc.contributor.author	Vandewalle, Joos
dc.date.accessioned	2014-10-07T03:10:23Z
dc.date.available	2014-10-07T03:10:23Z
dc.date.issued	1997
dc.identifier.citation	Tan, Shaohua,Zhang, Li,Vandewalle, Joos (1997). On the learning of min-max fuzzy systems. IEEE International Conference on Fuzzy Systems 3 : 1581-1584. ScholarBank@NUS Repository.
dc.identifier.uri	http://scholarbank.nus.edu.sg/handle/10635/81632
dc.description.abstract	This paper develops a methodology for learning fuzzy systems that contain min-max operations. It is shown that with the defuzzification properly defined, a min-max fuzzy system can have a transparent structure that is both universally approximating and easy for a learning scheme to be developed. A specific learning scheme based on multi-scale residue extraction is then presented.
dc.source	Scopus
dc.type	Conference Paper
dc.contributor.department	ELECTRICAL ENGINEERING
dc.description.sourcetitle	IEEE International Conference on Fuzzy Systems
dc.description.volume	3
dc.description.page	1581-1584
dc.description.coden	PIFSF
dc.identifier.isiut	NOT_IN_WOS
Appears in Collections:	Staff Publications

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