随机共振的模拟实验

用模拟电路的方法对随机共振现象进行多方面的实验研究,讨论随机共振机制,测量输出信噪比(SNR),由于在随机共振过程中部分噪声能量可转变为信号能量,利用这个机制可能在强噪声对信号的抑制下取出信号。 关键词：     

随机共振实验

介绍了随机共振现象的基本原理、模拟电路,给出了一组实验结果,并绘制了实验曲红,分析了随机共振产生的原因。     

随机共振研究新进展

介绍近10年来随机共振的几个重要研究方向取得的进展,对线性谐振子的随机共振研究,做了较详细的叙述.     

双稳随机共振参数特性的研究

以双稳系统为研究对象，探讨了影响和产生双稳随机共振的参数选择特性.双稳系统参数和二次采样频率参数有着内在的物理联系，它们的改变都会引起势垒、势阱间距和粒子跃迁速率的改变，并使噪声能量相对重新分配，进而影响随机共振效果.根据随机共振的参数选择规律，可通过适当的参数选择来得到最佳的随机共振状态，为后续数据处理奠定基础.     

过阻尼谐振子的随机共振

研究了由交叉相关高斯白噪声驱动的过阻尼谐振子的随机共振,其中加法噪声被周期信号所调制,运用平稳关联函数的傅里叶变换,导出了过阻尼谐振子随机模型信噪比的精确表达式.结果揭示:在过阻尼谐振子的随机模型中存在二类随机共振.一类随机共振表现为信噪比随乘法噪声强度Q变化的曲线存在共振峰,另一类随机共振表现为信噪比随振子频率ω变化的曲线存在共振峰.大幅度改变信号频率Ω值的大小,信噪比随乘法噪声强度Q变化的曲线有单峰,一峰一谷和单调变化三种不同的形式.     

一级近似下的双模激光随机共振

将周期信号输入双模激光,对激光光强方程中的乘法噪声项取一级近似,用线性化近似方法计算相关函数和功率谱,结果发现:信噪比随抽运噪声强度、量子噪声强度及信号频率的变化均出现随机共振.     

光学双稳系统中的随机共振

运用绝热近似理论，研究了由加性噪声和乘性噪声及周期信号驱动的光学双稳系统的随机共振现象. 发现该模型中输出信噪比R～随着加性噪声强度D_a的变化曲线中会出现随机共振现象，而信噪比R～随着乘性噪声强度D_m的变化曲线是单调减小的，信噪比曲线中没有出现随机共振现象. 因此，加性噪声和乘性噪声对输出信噪比的影响是不同的. 关键词： 随机共振 信噪比 乘性噪声 加性噪声     

基于振动共振的随机共振控制

分析了非线性双稳系统在高、低两种不同频率信号作用下的动力学特性，给出了高频信号参数与双稳系统输出信号的信噪比和功率谱放大率关系的解析表达式，提出了基于振动共振的随机共振控制方法.理论分析和数值仿真结果表明，通过调节高频信号的幅值或频率大小，能有效地控制双稳系统输出信号的信噪比和功率谱放大率.     

基于奇异值分解的随机共振特征提取研究

针对强背景噪声下信噪比极低的微弱特征信号的识别问题,提出了基于奇异值分解的随机共振特征提取方法.该方法首先利用奇异值分解对实际采样信号进行预处理和重构,然后寻找到特征信号分量与噪声强度相匹配的分量信号.此分量信号再经过非线性双稳系统的随机共振处理,可实现从强噪声背景中检测极微弱的特征信号.     

信号调制下分段噪声驱动的线性系统的随机共振

研究了由乘性和加性噪声的线性组合作为调制噪声的线性系统的随机共振.运用平均法,得到了一、二阶矩和信噪比的精确表达式. 通过分析信噪比,在此系统中发现了真实的随机共振、传统的随机共振和广义随机共振.发现在真实的随机共振中,适当地调整参数,共振和抑制会同时出现.此外,还发现对不同的互关联强度,分别选用乘性和加性噪声作为调制噪声能够提高信噪比. 关键词： 随机共振 信噪比 调制噪声 分段噪声     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Stochastic resonance in with additive noises a stochastic bistable system and square-wave signal

<正>This paper considers the stochastic resonance in a stochastic bistable system driven by a periodic square-wave signal and a static force as well as by additive white noise and dichotomous noise from the viewpoint of signal-to-noise ratio.It finds that the signal-to-noise ratio appears as stochastic resonance behaviour when it is plotted as a function of the noise strength of the white noise and dichotomous noise,as a function of the system parameters,or as a function of the static force.Moreover,the influence of the strength of the stochastic potential force and the correlation rate of the dichotomous noise on the signal-to-noise ratio is investigated.     

Coherent stochastic resonance in a system with in-out intermittency

We study the effect of noise on the properties of in-out intermittency. We add noise to a single variable of the system: the one parallel to the invariant subspace. At some magnitude of noise, a new kind of coherent stochastic resonance is observed in which the average time spent near the invariant subspace has a maximum as a function of the magnitude of the noise. We present numerical simulations of this phenomenon and a simple theoretical model using a Markov chain.     

基于随机共振原理的分段线性模型的理论分析与实验研究

提出了一种分段线性双稳态模型, 推导了模型的解析关系及其输出信噪比, 通过对该模型与连续双稳态模型的对比分析和仿真实验, 证明了该模型的优越性.该模型具有参数之间相互独立、易于调节的特点. 在对模型分析与数值仿真的基础上, 通过电路对强噪声背景下的微弱周期信号检测进行了实验研究. 结果表明分段线性随机共振模型能够有效实现对微弱周期信号的检测, 并能显著增强输出信噪比.     

二维映射神经元模型中频率依赖的随机共振

通过数值模拟方法, 研究了在具有稳定次阈值振荡特性的二维映射神经元体系中, 噪声对体系非线性动力学的调控作用. 通过计算发现了噪声诱导的动作电位和随机共振现象. 另外,还研究了体系的控制参数及输入信号的频率对体系动力学的影响, 发现了该体系中频率依赖的随机共振现象. 关键词： 二维映射神经元模型 次阈值振荡 高斯白噪声 随机共振     

Physics of neural synchronisation mediated by stochastic resonance

In this paper I discuss how synchronisation among neurons is affected by noise. Synchronisation of model neurons, which are basically autonomous relaxation oscillators, is enhanced by the addition of low levels of current noise and destroyed by the addition of higher levels of noise. Such neural stochastic resonance also occurs among neural networks in living systems at least at two functionally important scales, both within local networks and between the larger neuronal groups that are thought to implement complex cognitive and perceptual processes.