NeuroFAST: On-line neuro-fuzzy ART-based structure and parameter learning TSK model

Tzafestas, SG; Zikidis, KC

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Zikidis, KC	en
dc.date.accessioned	2014-03-01T01:16:47Z
dc.date.available	2014-03-01T01:16:47Z
dc.date.issued	2001	en
dc.identifier.issn	1083-4419	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14222
dc.subject	δ rule	en
dc.subject	Fuzzy ART learning	en
dc.subject	Structure/parameter identification	en
dc.subject	Takagi-Sugeno-Kang (TSK) fuzzy reasoning model	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Cybernetics	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Computer simulation	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Learning systems	en
dc.subject.other	Linear equations	en
dc.subject.other	Adaptive resonance theory (ART)	en
dc.subject.other	Neural networks	en
dc.title	NeuroFAST: On-line neuro-fuzzy ART-based structure and parameter learning TSK model	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/3477.956041	en
heal.identifier.secondary	http://dx.doi.org/10.1109/3477.956041	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding procedures. The well known delta rule continuously performs parameter identification on both premise and consequence parameters. Simulation results indicate the potential of the algorithm. It is worth noting that NeuroFAST achieves a remarkable performance in the Box and Jenkins gas furnace process, outperforming all previous approaches compared.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en
dc.identifier.doi	10.1109/3477.956041	en
dc.identifier.isi	ISI:000171543000013	en
dc.identifier.volume	31	en
dc.identifier.issue	5	en
dc.identifier.spage	797	en
dc.identifier.epage	802	en