Theoretical and Experimental Study of Order Estimation

dc.contributor.authorPan, J.en_US
dc.contributor.authorLevine, William S.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:50:21Z
dc.date.available2007-05-23T09:50:21Z
dc.date.issued1992en_US
dc.description.abstractWe consider estimation of system order and parameters for ARX systems with martingale difference noise. The order estimation criterion used is the accumulated prediction error (or predictive least-square error) criterion which generates strongly consistent order estimates. High computational load and over- complex (when real data are processed) models are two glaring problems occurring in order estimation. We develop a fast and parallel algorithm for strongly consistent estimation of system order and parameters. This shows that order estimation by minimizing the APE cost function could be performed on-line. We also modify the APE criterion by adding a device for tradeoffs between model complexity and model accuracy. This modification helps to solve the over-complexity problem. Furthermore, we present a simulation study on order estimation to complement the theoretical analysis.en_US
dc.format.extent1555908 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5213
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1992-31en_US
dc.subjectmathematical modelingen_US
dc.subjectestimationen_US
dc.subjectfilteringen_US
dc.subjectsignal processingen_US
dc.subjectadaptive controlen_US
dc.subjectfilteringen_US
dc.subjectstochastic systemsen_US
dc.subjectIntelligent Servomechanismsen_US
dc.titleTheoretical and Experimental Study of Order Estimationen_US
dc.typeTechnical Reporten_US

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