共查询到20条相似文献,搜索用时 187 毫秒
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研究在线盲信号分离问题.将适定盲信号分离的RLS算法,推广到源信号个数未知的超定盲信号分离模型中,利用正交投影方法消除了超定盲信号分离RLS算法的冗余移动,设计出能够稳定收敛的超定盲信号分离RLS算法.仿真实验验证了算法的有效性和其收敛的稳定性. 相似文献
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提出一种采用粒子群优化算法进行盲信号分离的新方法,为盲信号分离领域提供一种新的研究思路与方法。该方法采用峰度作为适应度函数,利用粒子群算法对由多个源信号混合而成的信号进行盲信号分离。与自然梯度法盲信号分离相比,粒子群算法精度更高,收敛速度更快,实例仿真成功地对两个图像混合信号进行了盲分离,表明了算法的有效性和优越性。 相似文献
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总结了源信号数目未知的盲信号分离自然梯度算法,得到自然梯度算法发散的原因,分离矩阵的各行沿混合矩阵转置的零空间方向无效的冗余移动。借助投影自然梯度算法,从理论上证明,冗余分量的范数随迭代次数的增加呈指数分布。 相似文献
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提出了一种多个信号源的超定盲信号分离算法,该方法利用奇异值分解来确定信号源的个数,并把天线阵的接收数据影射到正交的信号子空间中进行降维处理,再通过峰度自然对数最大化准则,对多个信号源按峰度减少的顺序依次进行分离.学习速率用非线性函数进行调节,避免了人为选取不当而导致的算法发散.该算法收敛速度快,且有较强的稳健性.计算机仿真验证了算法的有效性. 相似文献
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欠定盲信号分离技术是信号处理领域发展相对较晚的一种理论,目前已迅速成为该领域内重要的组成部分。本文首先对欠定盲信号的基本研究方法和分离问题的数学模型作了介绍,详细介绍了欠定盲信号的抽取算法,并指出其主要特点和性能。 相似文献
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Zhu Xiaolong Zhang Xianda 《电子科学学刊(英文版)》2006,23(3):399-403
This paper addresses the problem of Blind Source Separation (BSS) and presents a new BSS algorithm with a Signal-Adaptive Activation (SAA) function (SAA-BSS). By taking the sum of absolute values of the normalized kurtoses as a contrast function, the obtained signal-adaptive activation function automatically satisfies the local stability and robustness conditions. The SAA-BSS exploits the natural gradient learning on the Stiefel manifold, and it is an equivariant algorithm with a moderate computational load. Computer simulations show that the SAA-BSS can perform blind separation of mixed sub-Gaussian and super-Gaussian signals and it works more efficiently than the existing algorithms in convergence speed and robustness against outliers. 相似文献
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针对异步DS-CDMA系统存在强恶意干扰时,系统性能急剧下降的问题。本文提出一种采用双天线接收的盲联合干扰消除与多用户检测方案。不同于传统的多用户检测方法,该方案考虑了恶意干扰的影响,首先通过串并变换将恶意 干扰等效为多路信号,然后利用双天线接收到的两路受扰信号构造超定或适定盲源分离模型,将干扰消除与多用户检测问题转化为一相关源的盲分离问题,最后利用基于最大非高斯性盲源分离算法进行分离,从而实现盲联合干扰消除与多用户检测。该方案可以在未知扩频序列以及信道信息的情况下完成多用户检测,同时实现恶意干扰的消除,提高了系统的抗干扰能力。仿真结果表明该方案对单音干扰、部分频带干扰以及扫频干扰均具有一定的鲁棒性。 相似文献
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The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron’s outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm. 相似文献
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Xiao-Long Zhu Xian-Da Zhang Zi-Zhe Ding Ying Jia 《IEEE transactions on circuits and systems. I, Regular papers》2006,53(3):745-753
Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals. 相似文献
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在独立分量分析的相对梯度算法中,要取得较好的效果,选取合适的学习速率是至关重要的。对于这个问题,文章提出了一种可调速率的相对梯度算法,随着迭代次数的变化,使相对梯度算法的学习速率作相应变化,从而较好地解决了收敛速度与稳定性的矛盾。在此基础上,将这个方法应用于盲信号分离并进行仿真,得到了满意的结果。可调速率相对梯度算法在独立分量分析中具有较好的前景。 相似文献
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Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF‐SVD‐SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine‐tuning of centers and widths still shows ill‐behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center‐gradient variance of the RBFN‐SVD‐SG algorithm. We found analytically that the steady‐state weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center‐gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady‐state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance. 相似文献
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Yin F. Mei T. Wang J. 《IEEE transactions on circuits and systems. I, Regular papers》2007,54(5):1150-1158
In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstationary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms 相似文献