NeuroFAST: high accuracy neuro-fuzzy modeling

Tzafestas, S; Zikidis, K

dc.contributor.author	Tzafestas, S	en
dc.contributor.author	Zikidis, K	en
dc.date.accessioned	2014-03-01T02:49:10Z
dc.date.available	2014-03-01T02:49:10Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34365
dc.subject	Adaptive Resonance Theory	en
dc.subject	Chaotic Time Series	en
dc.subject	Function Approximation	en
dc.subject	Fuzzy Model	en
dc.subject	Fuzzy Rules	en
dc.subject	Learning Algorithm	en
dc.subject	Linear Equations	en
dc.subject	neuro fuzzy	en
dc.subject	Parameter Identification	en
dc.subject	Process Model	en
dc.subject	Structure Learning	en
dc.subject	First Order	en
dc.subject	takagi sugeno kang	en
dc.title	NeuroFAST: high accuracy neuro-fuzzy modeling	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICAIS.2002.1048093	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICAIS.2002.1048093	en
heal.publicationDate	2002	en
heal.abstract	Most fuzzy modeling algorithms rely either on simplistic (grid type) or off-line (trial-and-error type) structure identification methods. The proposed neurofuzzy modeling architecture, NeuroFAST, is an on-line, structure and parameter learning algorithm, featuring high function approximation accuracy. It is based on the first order Takagi-Sugeno-Kang (TSK) model (functional reasoning), where the consequence part of each fuzzy rule is a linear equation	en
heal.journalName	IEEE International Conference on Artificial Intelligence Systems	en
dc.identifier.doi	10.1109/ICAIS.2002.1048093	en