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dc.contributor.authorXu, L.en
dc.contributor.authorDuan, F.en
dc.contributor.authorGao, X.en
dc.contributor.authorAbbott, D.en
dc.contributor.authorMcDonnell, M.en
dc.identifier.citationRoyal Society Open Science, 2017; 4(9):1-12en
dc.description.abstractSuprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman-LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.en
dc.description.statementofresponsibilityLiyan Xu, Fabing Duan, Xiao Gao, Derek Abbott and Mark D. McDonnellen
dc.publisherRoyal Society Publishingen
dc.rights2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.en
dc.subjectKalman–least mean square; adaptive signal processing; recursive algorithm; suprathreshold stochastic resonanceen
dc.titleAdaptive recursive algorithm for optimal weighted suprathreshold stochastic resonanceen
dc.typeJournal articleen
pubs.library.collectionMedicine publicationsen
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]en
Appears in Collections:Medicine publications

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