Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

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...

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Bibliographic Details
Published in:	Royal Society open science Vol. 4; no. 9; p. 160889
Main Authors:	Xu, Liyan, Duan, Fabing, Gao, Xiao, Abbott, Derek, McDonnell, Mark D.
Format:	Journal Article
Language:	English
Published:	England The Royal Society Publishing 01.09.2017 The Royal Society
Subjects:	Adaptive Signal Processing Engineering Kalman–least Mean Square Recursive Algorithm Suprathreshold Stochastic Resonance recursive algorithm suprathreshold stochastic resonance adaptive signal processing Kalman–least mean square
ISSN:	2054-5703, 2054-5703
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2054-5703 2054-5703
DOI:	10.1098/rsos.160889