Publication:
Layer-recurrent network in identifying a nonlinear system

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dc.citedby6
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorNagi F.H.en_US
dc.contributor.authorid25930510500en_US
dc.contributor.authorid56272534200en_US
dc.date.accessioned2023-12-29T07:56:48Z
dc.date.available2023-12-29T07:56:48Z
dc.date.issued2008
dc.description.abstractLayer-Recurrent Network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applied and methodology on nonlinear system identification is presented where the best pair of input and output data is first measured. Using the simulated data, six LRN models are used to identify the satellite dynamics. It is shown that only 200 epochs are needed to train a network to converge to a reasonable mean squared value (mse). LRN output is then compared with the state space model where it shows that LRN model is capable to produce similar results as the state space satellite model without knowing the system's state and prior knowledge of the system.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4694674
dc.identifier.doi10.1109/ICCAS.2008.4694674
dc.identifier.epage391
dc.identifier.scopus2-s2.0-58149087554
dc.identifier.spage387
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-58149087554&doi=10.1109%2fICCAS.2008.4694674&partnerID=40&md5=c1cdf90349296f35a40489eebe771c07
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30964
dc.pagecount4
dc.sourceScopus
dc.sourcetitle2008 International Conference on Control, Automation and Systems, ICCAS 2008
dc.subjectLayer-Recurrent Network (LRN)
dc.subjectNonlinear input
dc.subjectNonlinear system identification
dc.subjectSatellite attitude
dc.subjectMathematical models
dc.subjectMetropolitan area networks
dc.subjectNavigation
dc.subjectNeural networks
dc.subjectNonlinear systems
dc.subjectSatellites
dc.subjectBlack box models
dc.subjectDynamic Neural networks
dc.subjectInput and outputs
dc.subjectLayer-Recurrent Network (LRN)
dc.subjectMean-squared values
dc.subjectNonlinear input
dc.subjectNonlinear system identification
dc.subjectOpen loop identifications
dc.subjectPrior knowledges
dc.subjectRecurrent networks
dc.subjectSatellite attitude
dc.subjectSatellite dynamics
dc.subjectSatellite models
dc.subjectSimulated datums
dc.subjectState Space models
dc.subjectState spaces
dc.subjectIdentification (control systems)
dc.titleLayer-recurrent network in identifying a nonlinear systemen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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