本文研究了后非线性混合信号的盲分离。
The problem of blind separation of signals in post nonlinear mixture is addressed in this paper.
信号的盲分离就是从一组由未知源信号混合得到的观测信号中估计源信号的过程。
Blind sources separation (BSS) is process of estimating unknown source signals from observed signals which are mixtures of unknown source signals.
最后，我们将方法应用于QAM调制信号的单混合信号的盲分离问题，得到较好的分离效果。
Finally, we apply this new method to the single mixture BSS for QAM signal, and get good result.
研究后非线性混合信号的盲分离，从最大似然角度推导了一般后非线性分离结构的学习公式；
The problem of blind separation of signals in post-nonlinear mixture is addressed. The learning rules for the general post-nonlinear separation structure are derived by a maximum likelihood approach.
该文分析了非线性混叠信号的可分离性及分离条件，指出现阶段非线性混叠信号盲分离的局限性。
The separability and separating conditions for mixed signals are analyzed in this paper. The limitation of nonlinear blind source separation methods is proposed.
一种新的实时线性混叠信号盲分离算法在本文提出。
A new kind of blind separation algorithm for real time linear mixture signals is presented in this paper.
基于空间时频分布的盲源分离算法可以用来分离具有不同时频分布的信号。
Blind source separation (BSS) based on spatial time frequency distribution can separate signals with different time frequency distributions.
本文提出一种基于双迭代方法的关于卷积混迭宽带非平稳有色信号的盲源分离算法。
In this paper, a convolutive blind source separation (BSS) algorithm based on a double-iteration method is proposed to process the convolutive mixed non-white broadband signals.
盲源分离是一个很有优势的盲信号分析与处理工具，在机械状态监测与故障诊断领域有较好的应用前景。
Blind sources separation is a special and dominant tool for analyzing and processing signals blindly, which is promising in condition monitoring and fault diagnosis of machinery.
本文针对盲信号分离中，如何根据信号特征进行有序提取的问题进行了探讨，提出了一种基于遗传算法的有序盲信号提取算法。
We have discussed about how to extract signal in older according to signals 'character in this paper, which is an important problem in the subject of blind separation.
采用基于二阶统计量的盲源分离算法对多导表面肌电信号进行处理，实现噪声的分离和表面肌电信号的初步分解。
Some methods of SEMG signal pretreatment based on blind source separation using second order statistics were proposed for noise separation and the elementary decomposition of multi-channel SEMG.
盲源分离是一个很独特的盲信号分析与处理工具，在机械设备状态监测与故障诊断领域有较好的应用前景。
Blind sources separation is a special tool for analyzing and processing signals blindly, which is promising in condition monitoring and fault diagnosis of machinery.
基于动态逼近的思想，提出了一种新型的信号盲分离迭代算法。
Based on dynamic approximation, this paper presents a new type of iterative algorithm to blind source separation.
盲源分离的目的在于只利用观测数据把被瞬时线性混合的源信号恢复出来。
Blind separation of sources consists of recovering a set of signals in which only instantaneous linear mixing are observed.
因为盲信号分离在通信，语音识别等相关领域的广泛应用，使得它成为近年来信号处理研究的热点问题之一。
Blind sources separation is becoming the problem that gets more and more focus because of broad application in many fields, such as communication, speech recognition and so on.
基于自然梯度原则并利用信号的时间相关属性对一类代价函数进行推导，获得一种新的非平稳信号自适应盲分离算法。
A new adaptive blind source separation algorithm of non-stationary signals was presented by using natural gradient rule and time-correlation property of the source signals acting on a cost function.
基于信号的白化变换及矩阵的QR分解理论，提出了一种新的盲分离算法。
A new blind separation algorithm based on signals' whiten transform and QR decomposition of matrix is proposed.
针对语音信号的弱稀疏性，提出一种新的基于混合矩阵估计的欠定语音盲分离方法。
This paper proposes a new method based on mixing matrix estimation for underdetermined blind speech separation, aiming at speech signals under weak sparseness.
基于提高被动声纳的检测能力，对水声信号的盲源分离技术进行了探索性研究。
To improve the detection performance of passive sonar, present dissertation addresses the blind separation of underwater acoustic signals.
本文提出了用盲信号分离提取胎儿心电的方法。
A new method of blind signal separation was provided to achieve FECG (Fetal ECG) detection.
本文利用基于高阶累积量的盲信号分离方法，设计了相应算法，成功地从被噪声污染的信号中恢复源振动信号，从而可确保碰摩故障的检测和诊断能顺利进。
Blind separation of sources based on higher order cumulants is applied to the separation between vibration signal and noise, which is important to diagnosi.
盲源分离应用于机械振动信号的预处理中，提供了一个新的处理机制，在机械状态监测和故障诊断中具有一定的价值。
Blind Sources Separation (BSS) provides a new alterative for extraction of certain signal components in the signal corrupted by noise, and presents a new method for mechanical fault diagnosis.
通过对传统盲源分离批处理EASI算法的分析，针对时变信道中通信信号的复数形式，以平滑窗的形式实现了批处理算法在时变混合模型下的应用。
Based on an analysis of EASI batch process algorithms for traditional blind source separation, a sliding window ICA algorithm is studied to deal with complex signals in the time variant mixing model.
主要的研究内容和成果包括以下几个方面：1利用定义双正交原理提出提出了多种用于阵列信号盲分离双二次代价函数。
The main contents and contributions include: 1 on basis of the defined biorthogonality, we propose several biquadratic cost functions for array-signal blind separation.
在许多盲信号源分离算法中，大多需要选择合适的非线性函数或者需要计算信号的高阶统计量。
In many algorithms for blind source separation, most of them must select nonlinear function or compute high-order statistical values.
利用各源信号之间的统计独立性和通信信号的循环平稳性，提出了一种适合于分布式天线接收的混合通信信号盲分离的思想和实现方法。
Based on the statistic independence of sources and the cyclo stationarity of communication signals, an idea and approach to separate the mixed signals received by distributed antennas is proposed.
计算机仿真结果表明该算法的收敛性能优于粒子群优化算法，并且在非线性盲信号分离中是有效的。
The computer simulation results showed that the proposed algorithm was superior to original particle swarm optimization algorithms and was effective in separating nonlinear blind sources.
基本思想是将现有的盲信源分离算法(BSS)应用到MIMO - OFDM系统的信号检测中。
The basic thought is to apply the existing blind source separation (BSS) algorithm to the signal detection in MIMO-OFDM systems.
研究了实际环境语音信号的特性，结合语音信号的短时平稳性和长时非平稳性，给出了一种时频域盲分离算法。
Investigation characteristic of real world audio, combine stationary for short time-scale and non-stationary for longer time-scales, proposed a time frequency domain blind source separation algorithm.
论文介绍了基于核空间的ICA的原理和基本算法，然后介绍了该算法与典型ICA和主成分分析(PCA)在盲源信号分离中的比较。
In this paper, kernel independent component analysis (KICA) 's principle and algorithm are introduced, and then the KICA comparison with some other ICA and principal component analysis (PCA) is given.
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