Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/114406
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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. |
DOI: | 10.1098/rsos.160889 |
Published version: | http://dx.doi.org/10.1098/rsos.160889 |
Appears in Collections: | Aurora harvest 3 Medicine publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_114406.pdf | Published Version | 644.64 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.