Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72831
DC FieldValue
dc.titleOn-line stable nonlinear modelling by structurally adaptive neural nets
dc.contributor.authorTan, Shaohua
dc.contributor.authorYu, Yi
dc.date.accessioned2014-06-19T05:12:30Z
dc.date.available2014-06-19T05:12:30Z
dc.date.issued1994
dc.identifier.citationTan, Shaohua,Yu, Yi (1994). On-line stable nonlinear modelling by structurally adaptive neural nets. IEEE International Conference on Neural Networks - Conference Proceedings 1 : 370-375. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72831
dc.description.abstractThis paper proposes a neural net based on-line scheme for modelling discrete-time multivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropriate on-line algorithms to carry out refinements for both the RBF net structure and the associated weights. Main convergence results are established in the paper along with the analysis backing up the structure initialization and adaptation. The effectiveness of the scheme is illustrated with an simulation example.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEEE International Conference on Neural Networks - Conference Proceedings
dc.description.volume1
dc.description.page370-375
dc.description.coden00176
dc.identifier.isiutNOT_IN_WOS
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