Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72681
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dc.titleIdentification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks
dc.contributor.authorTan, Shaohua
dc.contributor.authorHao, Jianbin
dc.contributor.authorVandewalle, Joos
dc.date.accessioned2014-06-19T05:10:48Z
dc.date.available2014-06-19T05:10:48Z
dc.date.issued1994
dc.identifier.citationTan, Shaohua,Hao, Jianbin,Vandewalle, Joos (1994). Identification of nonlinear discrete-time multivariable dynamical systems by RBF neural networks. IEEE International Conference on Neural Networks - Conference Proceedings 5 : 3250-3255. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72681
dc.description.abstractIn this paper, we propose a recursive identification technique for nonlinear discrete-time multivariable dynamical systems. Extending an early result to multivariable systems [8], the technique approaches a nonlinear system identification problem in two stages: One is to build up recursively a RBF (Radial-Basis-Function) neural net model structure including the size of the neural net and the parameters in the RBF neurons; the other is to design a stable recursive weight updating algorithm to obtain the weights of the net in an efficient way.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEEE International Conference on Neural Networks - Conference Proceedings
dc.description.volume5
dc.description.page3250-3255
dc.description.coden00176
dc.identifier.isiutNOT_IN_WOS
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