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 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
The Royal Society Publishing
01.09.2017
The Royal Society |
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| ISSN: | 2054-5703, 2054-5703 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Duan, Fabing Gao, Xiao Xu, Liyan Abbott, Derek McDonnell, Mark D. |
| AuthorAffiliation | 1 Institute of Complexity Science, Qingdao University , Qingdao 266071, People's Republic of China 3 Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering , The University of Adelaide, Adelaide , South Australia 5005, Australia 2 Computational and Theoretical Neuroscience Laboratory , Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia , Adelaide, South Australia 5095, Australia |
| AuthorAffiliation_xml | – name: 2 Computational and Theoretical Neuroscience Laboratory , Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia , Adelaide, South Australia 5095, Australia – name: 1 Institute of Complexity Science, Qingdao University , Qingdao 266071, People's Republic of China – name: 3 Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering , The University of Adelaide, Adelaide , South Australia 5005, Australia |
| Author_xml | – sequence: 1 givenname: Liyan orcidid: 0000-0003-4615-9183 surname: Xu fullname: Xu, Liyan organization: Institute of Complexity Science, Qingdao University, Qingdao 266071, People's Republic of China – sequence: 2 givenname: Fabing surname: Duan fullname: Duan, Fabing email: fabing.duan@gmail.com organization: Institute of Complexity Science, Qingdao University, Qingdao 266071, People's Republic of China – sequence: 3 givenname: Xiao surname: Gao fullname: Gao, Xiao organization: Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia 5095, Australia – sequence: 4 givenname: Derek orcidid: 0000-0002-0945-2674 surname: Abbott fullname: Abbott, Derek organization: Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia – sequence: 5 givenname: Mark D. surname: McDonnell fullname: McDonnell, Mark D. organization: Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia 5095, Australia; Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia |
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| Keywords | recursive algorithm suprathreshold stochastic resonance adaptive signal processing Kalman–least mean square |
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| SubjectTerms | Adaptive Signal Processing Engineering Kalman–least Mean Square Recursive Algorithm Suprathreshold Stochastic Resonance |
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| Title | Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance |
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