色噪声激励下三势阱系统逻辑随机共振研究

本文利用基于Simulink的数值模拟方法研究了高斯色噪声激励下三势阱系统的逻辑随机共振现象.首先对于独立的加性和乘性高斯色噪声激励下的三势阱系统,发现仅有加性噪声作用不能实现可靠的逻辑操作,但加性噪声和乘性噪声共同作用可诱导良好的逻辑随机共振现象.和高斯白噪声相比较,高斯色噪声激励下能产生可靠逻辑随机共振的（D,Q）平面上的区域范围更大.进一步讨论了加性和乘性噪声之间的关联对于逻辑随机共振现象的影响,发现噪声关联对逻辑随机共振现象起着破坏性的作用.     

多阈值系统中高斯混合噪声改善信息的传输

讨论高斯混合噪声下多阈值系统中的随机共振现象.对于单峰噪声,当输入信号在阈上时,互信息随着噪声的增强单调递减,噪声总是不利于信息的传输;当信号在阈下时,互信息随着噪声的增强先递增然后再递减,适量的噪声能改善信息传输,随机共振现象存在.对于双峰噪声,信号在阈下或阈上,噪声有时能够改善信息的传输,随机共振和阈上随机共振存在.这些结果说明多阈值系统中噪声改善信息的传输依赖于噪声类型,拓广了随机共振和阈上随机共振在多元信息传输中的应用.     

传感器阵列中阈上随机共振现象的仿真研究

研究了具有阈值特性的传感器阵列中的随机共振现象.使用相关系统数被来衡量输入随机信号和输出响应之间的非线性匹配程度.这一测度反映了输出与输入之间的信息转换率,因此可被用来考察阈值系统中的随机共振现象.文中从单个阈值单元模型出发,通过对输出信号进行估计,得出了噪声作用下输出随机变量与输入随机变量之间的相关系数.仿真结果表明在该类系统中,不但存在有阈值下的随机共振现象,而且也存在阈值上随机共振现象.     

相关乘性和加性高斯白噪声激励下周期势系统的随机共振

研究了外加周期信号作用下,相关高斯乘性和加性白噪声激励下周期势系统的随机共振.利用线性响应理论,计算了系统输出信号的功率谱密度、振幅、相位差.研究结果表明:当加性噪声强度和关联系数不变的情况下,通过调整乘性噪声强度可以出现随机共振;关联系数的正负以及大小对随机共振的影响较小.当乘性噪声强度较小时,输出信号的振幅和相位差曲线有一个单峰出现,即出现随机共振现象,能量从噪声向信号进行转化.随着噪声强度的增大,随机共振现象消失,噪声由增大系统的有序程度渐渐变为增大系统的无序程度.     

对称二值噪声下线性系统的随机共振

对于受到外部周期力、乘性和加性的二值噪声共同作用下的线性系统,通过运用Shapiro-Loginov公式,计算出系统一阶矩和信噪比的表达式。信噪比作为加性噪声强度D2的函数,数值结果表明在负相关区域-1≤λ〈0条件下有随机共振现象出现,而在非负相关区域0λ1却未出现;作为乘性噪声强度D1的函数时,仍在负相关区域-1≤λ〈0有共振现象发生,峰值高度随D2的增大而增大,位置右移;输入信号频率ω和外部力振幅α作为信噪比的变量时,随机共振现象也被发现,峰值的位置却不随着λ、α的变化而变化。     

基于互信息的多阈值系统中随机谐振现象研究

基于互信息讨论了四种典型噪声下多阈值系统中的随机谐振现象.当输入信号在阈上时,互信息随着噪声的增强而单调递减,噪声总是恶化系统的性能;而当输入信号在阈下时,互信息随着噪声的增强先递增然后再递减,适量噪声能改善多阈值系统的性能,随机谐振现象存在.随着阈值系统阈值的增大,随机谐振功效降低,最佳噪声强度逐渐增大.文中结果说明了多阈值系统中基于互信息的随机谐振现象存在.且随机谐振对噪声具有一定的鲁棒性,拓广了随机谐振在多元信号与信息处理中的应用.     

噪声改善码元传输

文中运用随机共振来改善码元的传输。对于由字符构成的文本,通过编码生成一系列的码元作为系统输入信号,经过带有噪声的系统传输后,进行译码得到接收文本。文中使用的噪声为高斯型的加性与乘性噪声,逐渐增加噪声强度,接收文本中出错字符比例先降低再增高,从而存在最佳噪声强度,此时出错比例最小,系统性能最好。另外,乘性噪声在改善信号传输时,表现出了一定的鲁棒性。最后,讨论了阈值单元数目与系统阈值的变化对系统性能的影响。     

一阶自回归模型中噪声改善信号的相关性

讨论一阶自回归模型中三种典型噪声改善信号的相关性问题。当输入信号在阈下或部分在阈下时,随着噪声强度的增加,输出信号与输入信号的相关系数先递增后递减,适量的噪声改善了信号的相关性,随机谐振现象存在。随着阈值的增加,随机谐振功效降低、最佳噪声值变大;随着噪声密度函数在零均值周边脉冲值变大和拖尾变厚,随机谐振功效也降低。存在一个噪声范围,其间输入信号与输出信号相关系数大于输入信号与噪声信号相关系数,一阶自回归模型中输出信号比噪声信号与输入信号更相关。这些结果说明在离散时间系统中噪声改善信号的相关性,随机谐振现象存在,且随机谐振对噪声具有鲁棒性。这些结果也拓广了随机谐振在数字信号处理中的应用。     

极大并联阈值网络中噪声改善信号的相关性

讨论了极大并联阈值网络中噪声改善信号相关性问题。当输入噪声为单峰高斯噪声时，输入信号在阈下时噪声才能改善信号的相关性，即随机谐振现象存在。而当输入噪声为双峰高斯混合噪声时，不仅输入信号在阈下时随机谐振现象有时存在，而且输入信号在阈上时噪声往往也能改善信号的相关性，即阈上随机谐振现象存在。噪声改善信号相关性随着网络中单元数的调整而改善。这些结果进一步说明了随机谐振或阈上随机谐振对噪声分布的依赖性，同时也拓广了随机谐振或阈上随机谐振在数字信号处理方面的应用。     

基于神经元阈上非周期随机共振机制的语音复原技术研究

应用随机共振机制.通过噪声能量来加强语音信号,改善低信噪比语音的输出质量.对FitzHugh-Nagumo(FHN)神经元模型中存在的阈上非周期随机共振现象进行了分析,根据其阈值特性,此二维神经元模型可被等价为两状态的阈值跨越非线性动力学系统.因此对含噪语音信号添加噪声,产生阈值化后的二值输出,经迭代收敛进入阈上非周期随机共振状态.在一个非零添加噪声强度上,含噪语音输出的互相关系数将达到最大值.通过语音复原的结果表明,本文方法对噪声的变化有更好的鲁棒性,尤其在强背景噪声下,随机共振方法较其他传统方法有更佳的复原效果.     

Stochastic resonance in parallel concatenated turbo code decoding

In an array of threshold devices, we examine the effect of noise in improving performance of turbo code decoding. Such a phenomenon of noise-enhanced effect is termed stochastic resonance (SR). When signal is subthreshold, SR is observed by using Gaussian noise during iterative decoding. That is, the minimal bit error ratio (BER) is achieved at some non-zero noise intensity level. Besides, the larger the number of threshold devices is, the more remarkable the SR effect becomes. Especially when noise intensity is nearly optimal, BER approximates to zero after a few decoding iterations. Moreover, when Gaussian mixture noise is utilized, suprathreshold stochastic resonance (SSR) occurs in turbo decoding. These results show the beneficial effect of noise in channel coding and decoding.     

离散时间系统中随机谐振研究

摘要：基于一种新的信噪比的定义讨论了在四种经典噪声下在离散时间系统中的随机谐振现象。当输入信号是阈上信号时,噪声总是在恶化信号的传输,即随着噪声强度增加,输出信噪比呈现单调递减的趋势。然而当输入信号是阈下信号时。适当的噪声能够引起系统最优反应,即随着噪声强度的增加,输出信噪比达到一个最大峰值,也即存在明显的随机谐振现象。随着非线性系统中阈值的增加,随机谐振现象的明显度降低,当最佳噪声强度在增加时,输出信噪比的最优值却在减少。文中结果也说明,基于新的信噪比的定义是可以用来度量一个离散时间系统的随机谐振现象,且随机谐振对噪声具有一定的鲁棒性,拓广了随机谐振在信息传输领域的应用。     

高阶统计量方法在谐波恢复中的应用

谐波恢复是信号处理中的重要问题,谐波恢复就是在噪声中分析组成信号的各次谐波.噪声分为加性噪声和乘性噪声,本文分析加性噪声和乘性噪声的特点,提出采用高阶统计量的方法抑制噪声,恢复谐波.通过四阶统计量的切片抑制乘性噪声,采用二阶累积量谱抑制加性噪声.仿真结果表明,本方法具有很好的抑制噪声效果.     

阈值阵列模型下的超阈值随机共振信噪比增益

研究了阈值阵列模型和超阈值随机共振现象.对该模型进行剖析,认为阈值阵列系统可以分解为单个阈值系统与集总平均器的级联.为了研究周期输入下的超阈值随机共振现象,理论分析了周期输入下的阈值阵列模型输出随机过程的统计特性,以输出信噪比增益作为随机共振的测度,固定输入信噪比,观测输出信噪比增益相对于阈值噪声方差的变化规律.证实当输入噪声为高斯噪声时,在阈值阵列系统中加入统计独立的高斯白噪声可使输出信噪比增益大于1,当输入噪声为非高斯噪声时,可获得更高的输出信噪比增益.     

Achieving a More Robust Neural Network Model for Control of a MR Damper by Signal Sensitivity Analysis

Most neural network models can work accurately on their trained samples, but when encountering noise, there could be significant errors if the trained neural network is not robust enough to resist the noise. Sensitivity to perturbation in the control signal due to noise is very important for the prediction of an output signal. The goal of this paper is to provide a methodology of signal sensitivity analysis in order to enable the selection of an ideal Multi-Layer Perception (MLP) neural network model from a group of MLP models with different parameters, i.e. to get a highly accurate and robust model for control problems. This paper proposes a signal sensitivity which depends upon the variance of the output error due to noise in the input signals of a single output MLP with differentiable activation functions. On the assumption that noise arises from additive/multiplicative perturbations, the signal sensitivity of the MLP model can be easily calculated, and a method of lowering the sensitivity of the MLP model is proposed. A control system of a magnetorheological (MR) fluid damper, which is a relatively new type of device that shows the future promise for the control of vibration, is modelled by MLP. A large number of simulations on the MR damper’s MLP model show that a much better model is selected using the proposed method.     

On robustness of radial basis function network with input perturbation

In this article, we have proposed a methodology for making a radial basis function network (RBFN) robust with respect to additive and multiplicative input noises. This is achieved by properly selecting the centers and widths for the radial basis function (RBF) units of the hidden layer. For this purpose, firstly, a set of self-organizing map (SOM) networks are trained for center selection. For training a SOM network, random Gaussian noise is injected in the samples of each class of the data set. The number of SOM networks is same as the number of classes present in the data set, and each of the SOM networks is trained separately by the samples belonging to a particular class. The weight vector associated with a unit in the output layer of a particular SOM network corresponding to a class is used as the center of a RBF unit for that class. To determine the widths of the RBF units, p-nearest neighbor algorithm is used class-wise. Proper selection of centers and widths makes the RBFN robust with respect to input perturbation and outliers present in the data set. The weights between the hidden and output layers of RBFN are obtained by pseudo inverse method. To test the robustness of the proposed method in additive and multiplicative noise scenarios, ten standard data sets have been used for classification. Proposed method has been compared with three existing methods, where the centers have been generated in three ways: randomly, using k-means algorithm, and based on SOM network. Simulation results show the superiority of the proposed method compared to those methods. Wilcoxon signed-rank test also shows that the proposed method is statistically better than those methods.