Convergence and error bounds of adaptive filtering under model structure and regressor uncertainties-中国期刊网

摘要 Adaptivefilteringalgorithmsareinvestigatedwhensystemmodelsaresubjecttomodelstructureerrorsandregressorsignalperturbations.Systemmodelsforpracticalapplicationsareoftenapproximationsofhigh-orderornonlinearsystems,introducingmodelstructureuncertainties.Measurementandactuationerrorscausesignalperturbations,whichinturnleadtouncertaintiesinregressorsofadaptivefilteringalgorithms.Employingordinarydifferentialequation(ODE)methodologies,weshowthatconvergencepropertiesandestimationbiascanbecharacterizedbycertaindifferentialinclusions.Conditionstoensurealgorithmconvergenceandboundsonestimationbiasarederived.Thesefindingsyieldbetterunderstandingoftherobustnessofadaptivealgorithmsagainststructuralandsignaluncertainties.

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Convergence and error bounds of adaptive filtering under model structure and regressor uncertainties

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Convergence and error bounds of adaptive filtering under model structure and regressor uncertainties

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