基于支持向量回归机的扩展卡尔曼滤波

在信号滤波算法优化问题的研究时,扩展卡尔曼滤波算法的精度依赖于系统模型精确性.采用一种改进的扩展卡尔曼滤波算法研究了状态量和观测量相同的系统.用滤波后的状态量和当前观测量以得到实时噪声,求出过程噪声方差阵,在此基础上利用支持向量回归机算法对过程噪声方差阵进行训练,从而得到新的过程噪声方差阵,用此阵作为下一次扩展卡尔曼滤波的过程方差阵,以后继续上述迭代过程.结果证明方法极大的提高了滤波精度.仿真说明方法的有效性.     

噪声和纹理图象的自适应(Adaptive)分割

本文提出了一种自适应的噪声和纹理图象分割算法.观察图象被模拟为由区域过程、映射过程和噪声过程三个层次综合作用构成的.整个算法包括两个独立的步骤:第一步是层次图象模型的参数估计算法,可以处理高斯噪声和出格点(Outlier)的混合噪声情况,因此具有鲁棒性.第二步是基于模型参数的图象分割算法,其核心是一个改进的多值模拟退火技术.计算机模拟实验证明了算法的有效性和鲁棒性.     

遥测噪声数据无损压缩算法研究

介绍了数据压缩的现状,分析了遥测噪声数据的特点和无损压缩算法的原理.根据噪声数据特点,通过各个算法比较和相关验证性实验,提出了Lz+ARC数据无损压缩算法,重点介绍了该无损压缩算法的原理和实现过程,并将该算法原理针对大量噪声数据进行了仿真压缩,结果表明压缩去除率可达到60%～90%.     

带有噪声递推估计的自适应集合卡尔曼滤波

卡尔曼滤波(KF)广泛应用于线性系统的状态估计问题.然而,它需要精确已知过程噪声的统计特性,这在实际应用中往往是不能满足的.在这个背景下,首先,根据协方差匹配原理建立一种带有过程噪声递推估计的自适应KF算法;然后,为了突破KF只能处理线性系统估计问题的局限,将过程噪声递推估计引入集合卡尔曼滤波(EnKF)中,提出一种自适应EnKF算法;最后,采用估计理论证明所提出算法的稳定性.与标准EnKF相比,该自适应算法在过程噪声统计特性未知的情况下滤波依然收敛,滤波精度及稳定性显著提升.仿真结果验证了所提出算法的有效性.     

融合克隆选择的AEA算法及其在参数估计中的应用

为了提高AEA算法的寻优性能,提出了结合克隆选择算法改进AEA算法计算过程中每代种群的生产方式.改进后的AEA算法(AEA-C)的克隆选择部分所加入的噪声是随着实际进化不同阶段而合理地变化.在进化的前期,由于添加噪声较大有利于算法的全局搜索,随着迭代次数增加,噪声逐渐减小,使得算法加强了局部搜索,算法能跳出局部最优,避...     

一种针对椒盐噪声的高速自适应中值滤波算法

大数据量高清视频流在拍摄、传输等过程中可能受到干扰而产生椒盐噪声.由于其具有数据传输速度快的特点,为了确保它的实时性,进一步提高滤波算法的时间效率和计算效率,对现有的自适应中值滤波进行了改进,提出了一种高速自适应中值滤波算法.滤波过程主要分为噪声点检测和噪声去除两个阶段.其中,在噪声点检测阶段,根据椒盐噪声的极值特性,将图像的像素点分为噪声点和信号点;在噪声去除阶段,信号点保持原值,噪声点根据自适应中值进行赋值.实验结果表明,该算法相较于多种中值滤波方法具有很好的滤波作用,以及很大的速度提升.     

一种快速点模型去噪方法

消除噪声是构造完美三维模型过程中必不可少的一步.胡国飞等提出了基于三维mean-shift过程的各向异性点模型去噪算法但该算法效率较低,为此提出用准柯西函数取代该算法所采用的高斯函数,提高了算法效率.实验结果表明,此方法能在有效剔除点模型表面噪声的同时较好地保持表面的尖锐特征.     

基于信息融合理论的卡尔曼滤波及仿真

对于存在控制及测量噪声,状态变量受到污染的系统,控制过程要求排除噪声干扰.本文采用信息融合理论,对传统卡尔曼滤波算法进行改进,得到新的滤波算法.有效的排除噪声干扰并通过仿真验证.     

自适应平方根无迹卡尔曼滤波算法

将高斯过程回归融入平方根无迹卡尔曼滤波(SRUKF)算法,本文提出了一种不确定系统模型协方差自适应调节滤波算法.该算法分为学习和估计两部分:学习阶段用高斯过程对训练数据进行学习,得到系统回归模型及噪声协方差;估计阶段由回归模型代替状态方程和观测方程,相应的噪声协方差实时自适应调整.该方法克服了传统方法容易受系统动态模型不确定性和噪声协方差不准确限制的问题,仿真结果验证了算法的有效性.     

自主机器人的噪声影响及其消解策略的研究

该文针对典型的觅食任务,以计算机仿真为手段,直观地揭示噪声对机器人系统性能的影响.在此基础上,提出了以过程奖赏（process reward）代替传统的结果奖赏（result reward）,并与优先扫除（prioritized sweeping）的强化学习算法结合作为噪声消解策略.然后与基于结果奖赏的Q学习算法（Q-learning）等其它四种算法进行比较,结果表明基于过程奖赏和优先扫除的强化学习算法能显著降低噪声的影响,提高了系统整体性能.     

ARMA systems excited by non-Gaussian processes are not alwaysidentifiable

It is a popular myth that the zeros of an ARMA (autoregressive moving-average) process excited by an unobservable non-Gaussian process can be resolved from noisy output measurements. A counterexample is given     

Parameter estimation from noisy measurements

This article considers the problem of estimating linear model parameters from noisy measurements. The starting point is the classical approach by Koopmans for linear regression analysis. It is known that concerning the direct application of those early results for process identification, neither the original Koopmans algorithm nor its updated forms called Koopmans–Levin algorithms exhibit maximum-likelihood (ML) parameter estimation. In this article, a new, numerically advanced method is developed to ensure ML property for the parameter estimation, assuming noisy inputs and outputs, respectively.     

Robust identification of continuous-time low-order models using moments of a single rectangular pulse response

This paper presents a simple method for identifying first- and second-order processes with dead-time by using moments of a single rectangular pulse response in an open-loop system. A closed-form formula is proposed to determine all parameters of four types of process models for a stable linear time-invariant process. It is shown that the same approach can be extended to the identification of multi-input, multi-output linear processes. It is demonstrated through a comparative analysis that the proposed identification method results in good accuracy with a noisy output, and is also able to closely approximate various high-order processes in those low-order models.     

Fuzzy cell Hough transform for curve detection

In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour point location which is increased when noisy images are used. By using fuzzy cells, each contour point in the spatial domain contributes in more than one fuzzy cell in the parameter space. The array that is created after the fuzzy voting process is smoother than in the crisp case and the effect of noise is reduced. The curves can now be detected with better accuracy. The computation time that is slightly increased by this method, can be minimized in comparison with classical Hough Transform, by using recursively the fuzzy voting process in a roughly split parameter space, to create a multiresolution fuzzily split parameter space.     

基于耳蜗核模型改进双耳时间差的声源定位

人耳听觉系统在噪声环境中能够准确定位感兴趣的声源,实现其定位的主要因素是双耳时间差,但是在噪声环境下利用双耳时间差方法进行定位的效果比较差。针对这一问题,提出一个基于耳蜗核模型的声源定位系统。利用耳蜗核模型模拟耳蜗核对听觉信息的处理机制,提取听觉神经纤维中对语声刺激同步的信息和发放率信息,从而实现对噪声的抑制,完成噪声环境下的声源定位。该系统在噪声环境下定位的误差为1.297°。实验结果证明改进之后的声源定位系统能在噪声环境下进行声源定位。     

噪声环境中时滞双向联想记忆神经网络指数稳定

任何系统实际上都是在噪声环境中进行工作的.对处在噪声强度已知的噪声环境下双向联想记忆(BAM)神经网络,其平衡点具有指数渐近稳定性是网络进行异联想记忆的基础.构造一个适当的Lyapunov函数,应用It^o公式、M矩阵等工具讨论了在噪声环境下具有时滞的BAM神经网络概率1指数渐近稳定,得到了指数稳定的代数判据和两个推论,此判据只需验证仅由网络参数构成的矩阵是M矩阵即可,给网络设计带来方便.本文所得结果包括相关文献中确定性结果作为特例.     

A robust automatic crack detection method from noisy concrete surfaces

In maintenance of concrete structures, crack detection is important for the inspection and diagnosis of concrete structures. However, it is difficult to detect cracks automatically. In this paper, we propose a robust automatic crack-detection method from noisy concrete surface images. The proposed method includes two preprocessing steps and two detection steps. The first preprocessing step is a subtraction process using the median filter to remove slight variations like shadings from concrete surface images; only an original image is used in the preprocessing. In the second preprocessing step, a multi-scale line filter with the Hessian matrix is used both to emphasize cracks against blebs or stains and to adapt the width variation of cracks. After the preprocessing, probabilistic relaxation is used to detect cracks coarsely and to prevent noises. It is unnecessary to optimize any parameters in probabilistic relaxation. Finally, using the results from the relaxation process, a locally adaptive thresholding is performed to detect cracks more finely. We evaluate robustness and accuracy of the proposed method quantitatively using 60 actual noisy concrete surface images.     

Using geometric spectral subtraction approach for feature extraction for DSR front-end Arabic system

Noise robustness and Arabic language are still considered as the main challenges for speech recognition over mobile environments. This paper contributed to these trends by proposing a new robust Distributed Speech Recognition (DSR) system for Arabic language. A speech enhancement algorithm was applied to the noisy speech as a robust front-end pre-processing stage to improve the recognition performance. While an isolated Arabic word engine was designed, and developed using HMM Model to perform the recognition process at the back-end. To test the engine, several conditions including clean, noisy and enhanced noisy speech were investigated together with speaker dependent and speaker independent tasks. With the experiments carried out on noisy database, multi-condition training outperforms the clean training mode in all noise types in terms of recognition rate. The results also indicate that using the enhancement method increases the DSR accuracy of our system under severe noisy conditions especially at low SNR down to 10 dB.     

基于Web挖掘的网页清洗技术

随着互联网上信息的大量增多,Web挖掘技术越来越重要。而在Web挖掘过程中,基于Web的信息抽取的主要部分是如何去除网页中的噪音数据,它是Web数据的预处理的过程,这个预处理结果影响了Web挖掘的结果。在文中先分析了噪音数据的特点,然后根据实际观察提取规则并且用于模型统计的方法,去除噪音数据,抽取相关可利用的信息。