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dc.contributor.advisorAbshire, Pamelaen_US
dc.contributor.authorZhai, Yimingen_US
dc.date.accessioned2005-02-02T06:51:05Z
dc.date.available2005-02-02T06:51:05Z
dc.date.issued2004-12-06en_US
dc.identifier.urihttp://hdl.handle.net/1903/2132
dc.description.abstractIn this thesis, an adaptive first order lowpass log domain filter and an adaptive second order log domain filter are presented with integrated learning rules for model reference estimation. Both systems are implemented using multiple input floating gate transistors to realize on-line learning of system parameters. Adaptive dynamical system theory is used to derive robust control laws in a system identification task for the parameters of both a first order lowpass filter and a second order tunable filter. The log domain filters adapt to estimate the parameters of the reference filters accurately and efficiently as the parameters are changed. Simulation results for both the first order and the second order adaptive filters are presented which demonstrate that adaptation occurs within milliseconds. Experimental results and mismatch analysis are described for the first order lowpass filter which demonstrates the success of our adaptive system design using this model-based learning method.en_US
dc.format.extent1086543 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleAdaptive Log Domain Filters Using Floating Gate Transistorsen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledEngineering, Electronics and Electricalen_US


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