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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Vydáno v:Royal Society open science Ročník 4; číslo 9; s. 160889
Hlavní autoři: Xu, Liyan, Duan, Fabing, Gao, Xiao, Abbott, Derek, McDonnell, Mark D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England The Royal Society Publishing 01.09.2017
The Royal Society
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2054-5703
2054-5703
DOI:10.1098/rsos.160889