基于ICA的白噪声消除方法研究

为了消除润滑油内金属磨粒检测系统(metal debris detection system,MDDS)输出信号中混合的高斯白噪声,提出了一个基于ICA的算法对两路输出信号进行消噪处理.对两路信号添加前缀信号,并按照所述步骤进行两次ICA后得到三路独立源信号,根据ICA前后前缀信号幅值和相位的变化校正ICA分离结果的幅值和相位,完全恢复源信号.对MDDS的输出信号进行仿真以验证算法的去噪效果,实验结果表明,该算法可以有效地消除输出信号中的白噪声.     

基于独立成分分析及其扩展模型的工业过程监测方法综述

独立成分分析(independent component analysis, ICA)是一种多变量统计分析方法,常用于非高斯过程监测,它能够有效利用信号的高阶统计信息(三阶以上)提取相互独立的独立成分,在工业过程监测中得到了广泛的应用,是当前国际过程监测领域的研究热点.鉴于此,介绍经典ICA模型、改进ICA模型及其在工业过程的过程监测技术.首先,对经典ICA模型进行介绍,在此基础上对经典ICA模型进行分类并指出其优缺点;其次,针对经典ICA模型存在的缺陷,从ICA自身存在的问题、噪声和离群值3方面梳理改进ICA模型的发展;然后,以工业过程为主要应用背景,介绍ICA的过程监测技术如何从简单工业过程衍变至复杂工业过程,以及面向工业过程运行数据的单一特性和混合特性,综述ICA及其扩展模型在工业过程监测中的研究现状;最后,探讨该研究领域亟需解决的问题和未来的发展方向.     

基于独立分量分析的电磁场信号工频干扰消除

为了解决电磁场信号测量中的工频及其谐波干扰问题,将盲源分离(BSS)应用于电磁场信号的工频干扰消除.从盲源分离和独立分量分析(ICA)的统一模型出发,分析了快速ICA算法和最大信噪比ICA算法的目标函数选择及算法推导,并分别对计算机随机产生不同波形信号和实测的电磁场信号进行分离,结果显示:无论从相似系数还是运算时间上看,最大信噪比ICA算法明显优于快速ICA算法.     

结合ICA预处理的麦克风阵列语音增强系统

在强背景噪声和强反射环境中,麦克风阵元接收的信号质量很差,从而影响麦克风阵列语音增强系统的性能.ICA能够仅从现测信号中提取出潜在的独立成分,基于此特性,本文将ICA引入麦克风阵列语音增强系统,利用ICA对麦克风阵元接收信号进行分析,从中提取出较纯净的目标语音作为系统的输入信号.ICA预处理可以有效抑制背景噪声和回声,提高输入信号的质量.真实环境中的实验表明,ICA预处理能够显著改善麦克风阵列语音增强系统的性能.     

核ICA在电流传感器相位差监测中的应用

介绍了核独立分量分析(ICA)的基本原理和算法,并将其用于对电流传感器输出的混合信号进行分离,通过比较分离出的单频测试信号输入前后的相位差,来标定传感器本身的相位差对其检测对象的影响。此外,还采用最大似然法对核ICA的分离效果进行评价。实验证明:在输入信号的信噪比为18.73dB的情况下,核ICA分离出的信号与源信号相位差在0.002 rad以内,达到了实际应用中所要求的误差范围。     

一种盲源分离的优先进化自适应遗传算法

盲分离技术与独立分量分析(ICA)由于不需要知道信号的先验信息而得到广泛应用.ICA是信号处理的一种新技术.其基本目标是寻找线性变换矩阵,将观测的多维混合信号进行变换,变换后的输出信号各分量之间尽可能统计独立.将改进的遗传算法(GA)与ICA相结合,提出基于优先进化自适应GA的盲源分离算法,并与传统的遗传算法进行了比较,证实了其具有更好的收敛性和稳态性能.对3段声音信号进行了仿真,仿真结果证明了算法的有效性.     

ICA算法在fMRI中的应用

独立分量分析（ICA）是信号处理领域新近发展起来的一种很有应用前景的方法,而脑功能磁共振（fMRI）信号的有效分离与识别是一个正在研究和实验之中的技术领域。近年来,ICA已被成功地应用于fMRI数据的处理,成为分析IMRI数据的一种很有效的方法。本文介绍了ICA在分析fMRI数据方面的应用,以及多种ICA算法在fMRI信号盲源分离中 的应用,分析了三种算法的问题,给出了本人对此研究的展望。     

ICA及其在事件相关电位P3中的应用

针对事件相关电位(Event-related Potential ,ERP)P3复合波中的亚成分较难分离的问题,运用近年新兴起来的独立分量分析(Independent Component Analysis ,ICA)算法对多道事件相关电位P3复合波进行了分解.实验结果表明:利用ICA可将混合在观测信号中的相互独立的源信号分离出来的特性,成功地分离出了P3复合波的各亚成分,从而表明了ICA在分离P3亚成分中的应用是一个值得注意的研究方向.     

免疫协同进化算法及其在模糊控制中的应用研究

针对基本遗传算法存在诸如早熟收敛、对搜索空间适应能力差等问题,提出了一种嵌入式协同进化算法,并引入免疫算子,提出了免疫协同进化算法(ICA)及其数学模型,同时给出了自适应交叉、变异算子.应用ICA对模糊控制器的隶属函数、控制规则以及量化因子比例因子三个物种进行协同优化;将该模糊优化控制方法应用锅炉汽包水位控制系统进行仿真研究,结果表明本文提出的应用ICA优化模糊控制器的有效性.     

基于独立分量分析的工频干扰消除技术*

简要介绍了ICA的基本原理和快速算法,在分析地震信号和工频干扰特点的基础上,利用ICA技术来消除地震记录中的工频干扰,并与常规方法进行比较。研究结果表明ICA在有效消除工频干扰的同时,能够保护有效信号,并且在提高资料的信噪比方面更有优势,具有良好的应用前景。     

独立分量分析及其在图像处理中的应用现状

独立分量分析是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,近年来作为信号处理和图像处理领域的强有力的分析处理工具得到广泛的关注和研究。在介绍了独立分量分析的基本概念和各种实现算法及其性能的基础上,综述了独立分量分析在图像处理上的应用,最后结合作者的研究探索,总结了独立分量分析的研究新进展和发展趋势。     

独立分量分析及其应用

独立分量分析是信号处理领域的一个研究热点,它能从混合信号中分离出既具有统计独立性又具有非高斯性的源信号。介绍了独立分量分析的数学模型及其假设条件、求解方法,在此基础上分析了其在语音信号处理、特征提取、生物医学信号处理等领域的应用,最后指出了其发展趋势与进一步的研究方向。     

A simplified approach to independent component analysis

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.     

基于ICA的周期性噪声消除算法

为了使问题有解，传统的独立分量分析算法对问题的条件有许多严格的限制，其中包括观测信号的个数不能小于源信号的个数等．在降噪等实际应用中，观测信号的个数可能无法满足这一条件，为了能够利用独立分量分析分离加性噪声，需要人工构造混合信号．基于周期性干扰表现的整体周期性，提出了一种构造混合信号的新算法．利用构造的混合信号进行独立分量分析，可以有效地消除周期性干扰，使目标信号的信噪比显著提高．即使在信噪比很低，目标信号几近被“淹没”的情况下，仍然能够较好地将其分离出来．该方法具有算法简单、运算速度快、算法效率高等特点．计算机仿真和实验结果都证明了算法的有效性．     

Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks

Independent component analysis (ICA) neural networks can estimate independent components from the mixed signal. The dynamical behavior of the learning algorithms for ICA neural networks is crucial to effectively apply these networks to practical applications. The paper presents the stability and chaotic dynamical behavior of a class of ICA learning algorithms with constant learning rates. Some invariant sets are obtained so that the non-divergence of these algorithms can be guaranteed. In these invariant sets, the stability and chaotic behaviors are analyzed. The conditions for stability and chaos are derived. Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior.     

EMD-ICA联合的信号处理方法研究

经验模态分解(EMD)方法和独立分量分析(ICA)方法在信号处理中各有优势与缺陷,详细介绍了这两种方法,深入分析了EMD-ICA联合技术在信号降噪和信号提取中的实现过程,并对该方法在电力工程、机械工程及地震工程中的研究应用作了详细介绍。     

A new method for fMRI data processing: Neighborhood independent component correlation algorithm and its preliminary application

Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.     

A fast algorithm for one-unit ICA-R

Independent component analysis (ICA) aims to recover a set of unknown mutually independent source signals from their observed mixtures without knowledge of the mixing coefficients. In some applications, it is preferable to extract only one desired source signal instead of all source signals, and this can be achieved by a one-unit ICA technique. ICA with reference (ICA-R) is a one-unit ICA algorithm capable of extracting an expected signal by using prior information. However, a drawback of ICA-R is that it is computationally expensive. In this paper, a fast one-unit ICA-R algorithm is derived. The reduction of the computational complexity for the ICA-R algorithm is achieved through (1) pre-whitening the observed signals; and (2) normalizing the weight vector. Computer simulations were performed on synthesized signals, a speech signal, and electrocardiograms (ECG). Results of these analyses demonstrate the efficiency and accuracy of the proposed algorithm.     

An independent component analysis-based disturbance separation scheme for statistical process monitoring

In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.     

一种结合信噪比的独立成分分析算法

针对传统独立成分分析算法存在的不足,在简要介绍独立成分分析的基本原理和相关算法的基础上,提出一种结合负熵与信噪比的独立成分分析法.推导了算法的关键公式,给出了实现算法,并进行了计算机仿真实验,分别使用传统算法和改进算法对模拟产生的合成数据进行分离.通过对实验结果进行的计算分析表明了所提出的改进算法比基于负熵的传统算法具有更佳的信号分离能力,能更好地从混合信号中估计出源信号.