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dc.contributor.authorAsyali, M.H.
dc.contributor.authorAlci, M.
dc.contributor.editor
dc.date.accessioned2021-01-25T20:52:11Z
dc.date.available2021-01-25T20:52:11Z
dc.date.issued2007
dc.identifier10.1007/978-3-540-36841-0_11
dc.identifier.issn16800737
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84907879147&doi=10.1007%2f978-3-540-36841-0_11&partnerID=40&md5=920291528159978885ce15abc22e2e8c
dc.identifier.urihttps://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10762
dc.description.abstractBoth neural networks (NN) and Volterra series (VS) are widely used in nonlinear dynamic system identification. In VS approach, the system is modeled using a set of kernel functions that correspond to different order convolutions. Kernels in VS are typical
dc.language.isoEnglish
dc.publisherIFMBE Proceedings
dc.titleObtaining volterra kernels from neural networks
dc.typeConference Paper
dc.relation.firstpage11
dc.relation.lastpage15
dc.relation.volume14
dc.relation.issue1
dc.description.affiliationsYasar University, Computer Engineering Dept, Bornova, Izmir, Turkey; Ege University, Electrical and Electronics Engineering Dept, Bornova, Izmir, Turkey


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