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Use of multilayer feedforward neural networks in identification and control of Wiener model

dc.contributor.authorChandrasekar, V., author
dc.contributor.authorKarim, M. N., author
dc.contributor.authorAl-Duwaish, H., author
dc.contributor.authorIEE, publisher
dc.date.accessioned2007-01-03T08:10:59Z
dc.date.available2007-01-03T08:10:59Z
dc.date.issued1996
dc.description.abstractThe problem of identification and control of a Wiener model is studied. The proposed identification model uses a hybrid model consisting of a linear autoregressive moving average model in cascade with a multilayer feed forward neural network. A two-step procedure is proposed to estimate the linear and nonlinear parts separately. Control of the Wiener model can be achieved by inserting the inverse of the static nonlinearity in the appropriate loop locations. Simulation results illustrate the performance of the proposed method.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.bibliographicCitationAl-Duwaish, H., M. N. Karim, and V. Chandrasekar, Use of Multilayer Feedforward Neural Networks in Identification and Control of Wiener Model, IEE Proceedings. Control Theory and Applications 143, no. 3 (May 1996): 255-258.
dc.identifier.urihttp://hdl.handle.net/10217/68053
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1996 IEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectnonlinear system identification
dc.subjectWiener model
dc.subjectneural networks
dc.titleUse of multilayer feedforward neural networks in identification and control of Wiener model
dc.typeText

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