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dc.contributor.authorLi, Rui.en_US
dc.description.abstractBlind equalization has been one of the most active areas of research in recent years. The potential application of blind equalization in wireless communication is one of the main reasons for its popularity. This thesis compares four different methods of blind equalization for nonminimum phase systems. Two Higher Order Statistics algorithms are used for channel identification. The first one is the Optimization al-gorithm and the second is Overdetermined Recursive Instrumental Variable (ORIV) algorithm. Two kinds of neural networks are used as equalizers to recover the trans-mitted signal. One is Multilayer Feedforward Network (MFN) based on Backpropa-gation algorithm, the other is Minimal Resource Allocation Network (MRAN) which is a newly developed Radial Basis Function Network that produces a parsimonious network structure.en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems-
dc.titleBlind equalization using neural networks and higher order statisticsen_US
dc.contributor.supervisorSaratchandran, Paramasivanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
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