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dc.contributor.authorKim, Jaesooen_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:06:37Z
dc.date.copyright1999-03en_NZ
dc.identifier.citationKim, 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/10523/1116en
dc.identifier.urihttp://hdl.handle.net/10523/1116
dc.descriptionPlease 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.abstractIn 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.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectneuro-fuzzy systemsen_NZ
dc.subjectneural networksen_NZ
dc.subjectfuzzy logicen_NZ
dc.subjectparameter and structure learningen_NZ
dc.subjectknowledge acquisitionen_NZ
dc.subjectadaptationen_NZ
dc.subjecttime seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleHybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systemsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages58en_NZ
otago.date.accession2011-01-10 20:43:14en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1022en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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