Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1434
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLam, HK-
dc.creatorLing, SH-
dc.creatorIu, HHC-
dc.creatorYeung, CW-
dc.creatorLeung, FHF-
dc.date.accessioned2014-12-11T08:26:25Z-
dc.date.available2014-12-11T08:26:25Z-
dc.identifier.isbn1-4244-1340-0-
dc.identifier.urihttp://hdl.handle.net/10397/1434-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectFeedback controlen_US
dc.subjectGenetic algorithmsen_US
dc.subjectNeural networksen_US
dc.subjectParameter estimationen_US
dc.titleControl of nonlinear systems with a linear state-feedback controller and a modified neural network tuned by genetic algorithmen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: H. H. C. Iuen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationCentre for Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents the control of nonlinear systems with a neural network. In the proposed neural network, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. By using a genetic algorithm with arithmetic crossover and non-uniform mutation, the parameters of the proposed neural network can be tuned. Application examples are given to illustrate the merits of the proposed neural network.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationCEC 2007 : IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007, p. 1614-1619-
dcterms.issued2007-
dc.identifier.scopus2-s2.0-79955284592-
dc.identifier.rosgroupidr39841-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
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