Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/19571
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dc.contributor.authorXu, Fangen_US
dc.date.accessioned2009-12-14T06:15:52Z
dc.date.available2009-12-14T06:15:52Z
dc.date.copyright1998en_US
dc.date.issued1998
dc.identifier.urihttp://hdl.handle.net/10356/19571
dc.description.abstractThis thesis contains three main results. The first result deals with an in-depth discussion of the radial basis function network where a training algorithm is proposed and the convergence of the RBF network is analyzed. The proof of convergence is based on the finite cover theory and the geometric growth criterion, which is the basis of the RBF network training algorithm.en_US
dc.format.extent111 p.
dc.language.isoen
dc.rightsNANYANG TECHNOLOGICAL UNIVERSITYen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
dc.titleTransient improvement via neural and switching controlen_US
dc.typeThesisen_US
dc.contributor.supervisorSoh, Yeng Chaien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
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