Xu Liyan, Duan Fabing, Gao Xiao, Abbott Derek, McDonnell Mark D
Institute of Complexity Science, Qingdao University, Qingdao 266071, People's Republic of China.
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.
R Soc Open Sci. 2017 Sep 13;4(9):160889. doi: 10.1098/rsos.160889. eCollection 2017 Sep.
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.
超阈值随机共振（SSR）是一种独特的随机共振形式，它发生在多级并行阈值阵列中，对信号强度没有要求。在一般的SSR模型中，一种最优加权解码方案在最小化均方误差（MSE）方面显示出其优越性。在本研究中，我们通过结合卡尔曼滤波器和最小均方（LMS）递归算法，将所提出的最优加权解码方案扩展到更一般的输入特性，其中加权系数可以自适应调整，以便在不完全了解输入统计信息的情况下最小化MSE。我们证明，基于卡尔曼 - LMS递归算法的最优加权解码方案能够稳健地解码观察到SSR的系统的输出，即使在信号和噪声随时间变化的复杂情况下也是如此。
