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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLam, HK-
dc.creatorLeung, FHF-
dc.rights© 2005 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.subjectClosed loop control systemsen_US
dc.subjectFeedforward neural networksen_US
dc.subjectGenetic algorithmsen_US
dc.subjectNonlinear systemsen_US
dc.subjectSystem stabilityen_US
dc.titleDesign and stabilization of sampled-data neural-network-based control systemsen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformation"Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering"en_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents the design and stability analysis of sampled-data neural-network-based control systems. A continuous-time nonlinear plant and a sampled-data three-layer fully-connected feed-forward neural-network-based controller are connected in a closed-loop to perform a control task. Stability conditions will be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches will be employed to obtain the maximum sampling period and connection weights of the neural network subject to the considerations of the system stability and performance. An application example will be given to illustrate the design procedure and effectiveness of the proposed approach.-
dcterms.bibliographicCitation2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 2249-2254-
dc.description.ros2005-2006 > Academic research: refereed > Refereed conference paper-
Appears in Collections:Conference Paper
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