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dc.contributor.authorKasabov, Nikolaen_NZ
dc.contributor.authorSong, Qunen_NZ
dc.date.available2011-04-07T03:05:11Z
dc.date.copyright1999-03en_NZ
dc.identifier.citationKasabov, N., & Song, Q. (1999). Dynamic evolving fuzzy neural networks with `m-out-of-n’ activation nodes for on-line adaptive systems (Information Science Discussion Papers Series No. 99/04). University of Otago. Retrieved from http://hdl.handle.net/10523/844en
dc.identifier.urihttp://hdl.handle.net/10523/844
dc.description.abstractThe paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, hybrid (supervised/unsupervised), on-line learning, like the EFuNNs. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. At each time moment the output vector of a mEFuNN is calculated based on the m-most activated rule nodes. Two approaches are proposed: (1) using weighted fuzzy rules of Zadeh-Mamdani type; (2) using Takagi-Sugeno fuzzy rules that utilise dynamically changing and adapting values for the inference parameters. It is proved that the mEFuNNs can effectively learn complex temporal sequences in an adaptive way and outperform EFuNNs, ANFIS and other neural network and hybrid models. Rules can be inserted, extracted and adjusted continuously during the operation of the system. The characteristics of the mEFuNNs are illustrated on two bench-mark dynamic time series data, as well as on two real case studies for on-line adaptive control and decision making. Aggregation of rule nodes in evolved mEFuNNs can be achieved through fuzzy C-means clustering algorithm which is also illustrated on the bench mark data sets. The regularly trained and aggregated in an on-line, self-organised mode mEFuNNs perform as well, or better, than the mEFuNNs that use fuzzy C-means clustering algorithm for off-line rule node generation on the same data set.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectdynamic evolving fuzzy neural networksen_NZ
dc.subjecton-line learningen_NZ
dc.subjectadaptive controlen_NZ
dc.subjectdynamic time series predictionen_NZ
dc.subjectfuzzy clusteringen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleDynamic evolving fuzzy neural networks with `m-out-of-n' activation nodes for on-line adaptive systemsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages29en_NZ
otago.date.accession2010-11-02 20:22:34en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints988en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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