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Estimation of Hidden Markov Models for Partially Observed Risk Sensitive Control Problems

dc.contributor.authorFrankpitt, Bernard A.en_US
dc.contributor.authorBaras, John S.en_US
dc.date.accessioned2007-05-23T10:04:04Z
dc.date.available2007-05-23T10:04:04Z
dc.date.issued1997en_US
dc.identifier.urihttp://hdl.handle.net/1903/5867
dc.description.abstractWe look at the problem of estimation for partially observed, risk-sensitive control problems with finite state, input and output sets, and receding horizon. We describe architectures for risk sensitive controllers, and estimation, and we state conditions under which both the estimated model converges to the true model, and the control policy will converge to the optimal risk sensitive policy.en_US
dc.format.extent176764 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1997-39en_US
dc.subjectqueueing networksen_US
dc.subjectsignal processingen_US
dc.subjectoptimizationen_US
dc.subjectdiscrete event dynamical systems en_US
dc.subjectmachine learningen_US
dc.subjectpurely theoreticalen_US
dc.subjectnonlinear stochasticen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleEstimation of Hidden Markov Models for Partially Observed Risk Sensitive Control Problemsen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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