A New On-Line Structure and Parameter Learning Architecture for Fuzzy Modeling, Based on Neural and Fuzzy Techniques

Tzafestas, S; Zikidis, K

dc.contributor.author	Tzafestas, S	en
dc.contributor.author	Zikidis, K	en
dc.date.accessioned	2014-03-01T02:48:35Z
dc.date.available	2014-03-01T02:48:35Z
dc.date.issued	1998	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33927
dc.subject	Computer Simulation	en
dc.subject	Fuzzy Model	en
dc.subject	Fuzzy Reasoning	en
dc.subject	Fuzzy Rules	en
dc.subject	Supervised Learning	en
dc.subject	Neural Network	en
dc.subject	Output Error	en
dc.subject	takagi sugeno kang	en
dc.title	A New On-Line Structure and Parameter Learning Architecture for Fuzzy Modeling, Based on Neural and Fuzzy Techniques	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/3-540-64574-8_423	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-64574-8_423	en
heal.publicationDate	1998	en
heal.abstract	Functional reasoning or the Takagi-Sugeno-Kang model is a fuzzy reasoning method aiming at numerical accuracy and has found wide use in fuzzy modeling. In this method, each rule consists of a fuzzy implication and a functional consequence part. In this work, a new, online identification method for such a system is presented, for supervised learning tasks. Structure identification is executed	en
heal.journalName	Industrial and Engineering Applications of Artificial Intelligence and Expert Systems	en
dc.identifier.doi	10.1007/3-540-64574-8_423	en