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dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:06:39Z
dc.date.copyright1999-03en_NZ
dc.identifier.citationKasabov, N. (1999). Evolving connectionist systems for on-line, knowledge-based learning: Principles and applications (Information Science Discussion Papers Series No. 99/02). University of Otago. Retrieved from http://hdl.handle.net/10523/1122en
dc.identifier.urihttp://hdl.handle.net/10523/1122
dc.description.abstractThe paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. 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. The ECOS framework is presented and illustrated on a particular type of evolving neural networks---evolving fuzzy neural networks (EFuNNs). EFuNNs can learn spatial-temporal sequences in an adaptive way, through one pass learning. Rules can be inserted and extracted at any time of the system operation. The characteristics of ECOS and EFuNNs are illustrated on several case studies that include: adaptive pattern classification; adaptive, phoneme-based spoken language recognition; adaptive dynamic time-series prediction; intelligent agents.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectevolving connectionist systemsen_NZ
dc.subjecton-line learningen_NZ
dc.subjectspatial-temporal adaptationen_NZ
dc.subjectadaptive speech recognitionen_NZ
dc.subjectEvolving Fuzzy Neural Networksen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleEvolving connectionist systems for on-line, knowledge-based learning: Principles and applicationsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages33en_NZ
otago.date.accession2010-11-10 20:38:10en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints986en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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