Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems

dc.contributor.author	Kim, Jaesoo	en_NZ
dc.contributor.author	Kasabov, Nikola	en_NZ
dc.date.copyright	1999-03	en_NZ
dc.identifier.citation	Kim, J., & Kasabov, N. (1999). Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems (Information Science Discussion Papers Series No. 99/05). University of Otago. Retrieved from http://hdl.handle.net/10523/1116	en
dc.identifier.uri	http://hdl.handle.net/10523/1116
dc.description	Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.	en_NZ
dc.description.abstract	In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme composed of two phases: the phase of rule generation from data, and the phase of rule tuning by using the error backpropagation learning scheme for a neural fuzzy system. In order to illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamics are carried out. The proposed method can be applied to on-line incremental adaptive leaning for the purpose of prediction and control of non-linear dynamical systems.	en_NZ
dc.format.mimetype	application/pdf
dc.publisher	University of Otago	en_NZ
dc.relation.ispartofseries	Information Science Discussion Papers Series	en_NZ
dc.subject	neuro-fuzzy systems	en_NZ
dc.subject	neural networks	en_NZ
dc.subject	fuzzy logic	en_NZ
dc.subject	parameter and structure learning	en_NZ
dc.subject	knowledge acquisition	en_NZ
dc.subject	adaptation	en_NZ
dc.subject	time series	en_NZ
dc.subject.lcsh	QA76 Computer software	en_NZ
dc.title	Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems	en_NZ
dc.type	Discussion Paper	en_NZ
dc.description.version	Unpublished	en_NZ
otago.bitstream.pages	58	en_NZ
otago.date.accession	2011-01-10 20:43:14	en_NZ
otago.school	Information Science	en_NZ
otago.openaccess	Open
otago.place.publication	Dunedin, New Zealand	en_NZ
dc.identifier.eprints	1022	en_NZ
otago.school.eprints	Knowledge Engineering Laboratory	en_NZ
otago.school.eprints	Information Science	en_NZ
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otago.relation.number	99/05	en_NZ