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dc.contributorDepartment of Electrical Engineering-
dc.creatorCheng, KWE-
dc.creatorWang, HY-
dc.creatorSutanto, D-
dc.rights© 1999 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.subjectAdaptive control systemsen_US
dc.subjectComputer simulationen_US
dc.subjectElectric currentsen_US
dc.subjectElectric distortionen_US
dc.subjectElectric power factoren_US
dc.subjectElectric waveformsen_US
dc.subjectLearning algorithmsen_US
dc.subjectLyapunov methodsen_US
dc.subjectMathematical modelsen_US
dc.subjectOnline systemsen_US
dc.subjectPower convertersen_US
dc.subjectPulse width modulationen_US
dc.titleAdaptive B-spline network control for three-phase PWM AC-DC voltage source converteren_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: D. Sutantoen_US
dcterms.abstractA neural network control method - adaptive B-spline neural network for three-phase AC-DC voltage source converters that realizes a sinusoidal ac input current and unity power factor is discussed in this paper. Comparing to the other PWM techniques, the main advantage of the neural network is that it has excellent merit for nonlinear control and is adaptive enough to fit the environment change. Since the training for the network is on-line in this paper, it is more robust to external disturbances. B-spline neural network is used because it is characterized by a local weight updating scheme with the advantages of fast convergence speed and low computation complexity. This is fairly important for real-time control application. The stability of the network control strategy can be shown using Lyapunov law. Simulation results are presented to illustrate the effectiveness of the proposed control strategy.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationPEDS'99 : proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems : 27-29 July 1999, Hong Kong, p. 467-472-
dc.relation.ispartofbookPEDS'99 : proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems : 27-29 July 1999, Hong Kong-
dc.description.oaVersion of Recorden_US
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