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Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.sysconle.2004.02.016
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dc.titleAdaptive MNN control for a class of non-affine NARMAX systems with disturbances
dc.contributor.authorGe, S.S.
dc.contributor.authorZhang, J.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:36:51Z
dc.date.available2014-06-17T02:36:51Z
dc.date.issued2004-09
dc.identifier.citationGe, S.S., Zhang, J., Lee, T.H. (2004-09). Adaptive MNN control for a class of non-affine NARMAX systems with disturbances. Systems and Control Letters 53 (1) : 1-12. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sysconle.2004.02.016
dc.identifier.issn01676911
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54904
dc.description.abstractIn this paper, adaptive multi-layer neural network (MNN) control is developed for a class of discrete-time non-affine nonlinear systems in nonlinear auto regressive moving average with eXogenous inputs (NARMAX) model. By using implicit function theorem, the existence of the implicit desired feedback control (IDFC) is proved. MNNs are used as the emulator of the desired feedback control. Projection algorithms are used to guarantee the boundedness of the neural network (NN) weights, which removes the need of persistent exciting (PE) condition for parameter convergence. Simulation results show the effectiveness of the proposed control scheme. © 2004 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.sysconle.2004.02.016
dc.sourceScopus
dc.subjectImplicit function theorem
dc.subjectMulti-layer neural networks
dc.subjectNon-affine nonlinear system
dc.subjectProjection algorithm
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentBIOENGINEERING
dc.description.doi10.1016/j.sysconle.2004.02.016
dc.description.sourcetitleSystems and Control Letters
dc.description.volume53
dc.description.issue1
dc.description.page1-12
dc.description.codenSCLED
dc.identifier.isiut000223114900001
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