Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114406
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Type: Journal article
Title: Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance
Author: Xu, L.
Duan, F.
Gao, X.
Abbott, D.
McDonnell, M.
Citation: Royal Society Open Science, 2017; 4(9):1-12
Publisher: Royal Society Publishing
Issue Date: 2017
ISSN: 2054-5703
2054-5703
Statement of
Responsibility: 
Liyan Xu, Fabing Duan, Xiao Gao, Derek Abbott and Mark D. McDonnell
Abstract: Suprathreshold 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.
Keywords: Kalman–least mean square; adaptive signal processing; recursive algorithm; suprathreshold stochastic resonance
Rights: 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
RMID: 0030075983
DOI: 10.1098/rsos.160889
Appears in Collections:Medicine publications

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