基于UPF的INS非高斯噪声故障检测

由于实际组合导航系统很可能受到非高斯噪声的影响,而传统的故障检测方法对非高斯噪声情况讨论较少.基于粒子滤波的故障检测技术非常适合处理非线性、非高斯问题,并且有效地克服了传统方法的不足,但是普通的粒子滤波器存在粒子“退化”等问题.为此,本文提出了一种基于UPF滤波器的导航系统故障检测方法,通过在普通粒子滤波中引入UKF产生建议分布及重采样的方法,有效抑制了普通粒子滤波器粒子“退化”的问题.并针对噪声的非高斯特性,将似然检测方法与粒子滤波可以估计似然函数的特点相结合,提出了一种基于UPF的故障检测方法.通过GPS/SINS组合导航系统在噪声服从瑞利分布情况下的故障检测仿真实例,表明此方法适用于在非高斯噪声情况下的导航系统故障检测.     

基于模糊似然函数的模糊辨识方法

提出一种基于模糊似然函数的模糊辨识方法。该方法利用模糊似然函数对样本数据进行聚类，并使模糊模型的结构辨识和参数辨识能同时完成，从而实现模糊模型的在线辨识。仿真结果证明了该方法的有效性。     

基于自适应自然梯度法的在线高斯过程建模*

为了满足在线建模算法的实时性要求,提出了在高斯过程的训练中使用自适应自然梯度法（ANG）,即基于自适应自然梯度法的在线高斯过程回归建模算法。将此算法运用在Micky-Glass系统和连续搅拌反应釜(CSTR)模型的建立中,并与稀疏在线高斯过程算法进行比较。仿真结果表明此算法满足了非线性系统建模的实时性和精度的要求,同时克服了其他方法计算量很大、不符合在线算法的实时性要求的缺点。     

血液循环系统的建模与分析*

为了分析心血管辅助装置与自然心脏的相互作用,本文从系统辨识的方法出发,建立从左心室到主动脉的变结构线性时变系统模型,对建立的模型进行了仿真,计算结果表明,这一建模方法是成功的,模型在一定范围内有效     

水处理系统的神经网络建模研究

对以臭气化一生物活性炭技术为基础的水质处理系统的辨识问题进行研究,给出了基于ＢＰ神经网络的建模方案。采用水质处理系统中的ＣＯＤＭ和臭氧投放量对某水质处理系统进行建模,实验结果证明了该方法的有效性。     

基于模糊规则的非线性系统建模方法

提出了一种基于模糊聚类自调整的模糊建模方法,基于模糊聚类通过自适应模糊推理来调整模糊系统,一种在线辨识算法的是通过非线笥系统参数的在线性估计来进行的,为了证明了所提出方法的适用性,给出了几个实例的仿真结果。     

基于高斯混合模型聚类的非均匀采样系统的多模型切换辨识

从概率统计方法出发,提出一种基于高斯混合模型聚类与递推最小二乘算法的非均匀采样系统的多模型建模方法.首先,采用高斯混合模型作为调度函数,使用最大期望(EM)算法迭代更新估计高斯混合模型中参数,从而通过每个子系统的高斯概率密度函数计算和比较来确定子系统的激活情况; 其次,采用递推最小二乘算法估计局部子系统参数;然后,使用鞅收敛定理对所提出的算法性能进行分析; 最后,通过非均匀采样系统的多模型建模来证明所提出方法的有效性.     

状态缺失多变量系统的极大似然辨识方法

提出一种极大似然辨识方法,用于解决状态缺失多变量系统的参数估计问题。通过构造以输入-输出序列为条件概率的似然函数 表达式,以及分析数据缺失程度对参数估计的影响,设计适用于状态缺失情况的卡尔曼状态估计器,在此基础上提出极大化似然函数的参数计算方法。数值仿真结果证明了该方法的有效性。     

基于高斯混合模型的DCT域水印检测方法

基于音频DCT系数的统计特征,提出了一种水印检测方法.采用扩频的方法在DCT域嵌入水印, 分别对嵌入水印和未包含水印的音频信号在DCT域进行统计学习,得到对应的高斯混合模型(Gaussian mixture model, GMM). 接收端采用最大似然检测,判断是否嵌入水印并提取相应的水印信息. 仿真结果表明本文的水印检测算法对常见的信号攻击具有鲁棒性,与传统的相关检测法相比,检测可靠性更高.     

高斯极大似然估计的新冠疫情周期性预测分析

新型冠状肺炎病毒自2019年末被发现以来,就已经被视为一场全面的大流行,并在全世界蔓延。许多研究正在使用独特的模型来预测与分析这种疾病在未来的趋势和可能的进展。通过评估极大似然估计求解高斯拟合参数,并利用高斯—牛顿迭代法优化求解参数的高斯模型来预测上海每日新增新冠肺炎病例与疫情病例周期的转折点。实验证明,算法的拟优度R2达到0.9286,表明该算法在新冠疫情病例数据分析与预测领域的有效性、可靠性和优越性。     

一种非线性非高斯随机系统的故障诊断方法

针对非线性非高斯随机系统在线故障诊断的问题,运用粒子滤波器提出了一种基于方差自适应粒子滤波器的非线性非高斯随机系统的故障诊断方法,可以用来解决系统的参数偏差型故障诊断问题。通过对连续搅拌釜式反应器(CSTR)的仿真研究,可以看出,该算法实现简单,易于对系统进行在线估计,对于发生缓变和突变的参数偏差型故障的检测与估计均较为有效。     

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise. The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system. Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering, we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points, reduces distant...     

非高斯背景下的频域广义匹配检测

采用广义匹配滤波器在功率谱未知的非高斯噪声中检测宽带信号。在估计功率谱时,为减小周期图法谱估计产生的误差,采用了平滑周期图法来估计非高斯噪声的功率谱,并在此基础上对信号进行恒虚警概率损失下的广义匹配滤波检测。实验证明,用平滑周期图法估计功率谱并进行的广义匹配检测,可以得到较好的检测效果。其检测性能稍弱于功率谱取真值时的检测,但远远优于传统的匹配滤波器。这一结论用到处理复杂的声纳和雷达信号,在实际应用中有一定的指导意义。     

Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of statemultiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion(MCC) which describes the similarity between two stochastic ...     

Nonlinear Filtering of Non-Gaussian Noise

This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.     

On Advances in Statistical Modeling of Natural Images

Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.     

控制系统性能诊断—高阶统计量方法

针对控制系统性能的下降将直接导致设备、生产过程的异常,以及产品质量下降的问题,通过对某矿长钢丝绳胶带输送机尾部张力控制系统的分析,研究了控制系统性能的诊断问题。采用高阶统计量方法来分析控制系统性能下降的原因,提出了诊断控制系统性能下降原因的算法,并将该算法应用于实际工业系统。结果显示,高阶统计量包含了丰富的信息,能有效地反映非线性程度的强弱,在控制系统性能诊断方面具有一定的优越性,说明了该算法的可行性和有效性。     

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems: Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

     

非高斯噪声下输出残差或状态估计误差的熵研究

基于非Gaussian噪声线性定常控制系统,通过控制滤波器输出残差或状态估计误差的条件概率密度函数形状来建立有效的滤波设计算法,创建滤波器输出残差或状态估计误差的条件概率密度函数的统一表现形式。利用复合概率密度函数的关系对残差或状态估计误差的条件概率密度函数的近似来实现非高斯残差的高斯化或相应的熵最小化。