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.
提出了基于相关性的瞬时线性混合盲信号分离算法。
An algorithm based on the correlation characters for instantaneous linear mixture of the source signal is proposed.
本文用双层的细胞神经网络(CNN)进行盲信号分离。
In this paper a two-layer cellular neural network (CNN) is used to separate blind signals.
在盲信号分离中，用串音误差：作为衡量信号分离的性能指标。
In the blind signal separation, with the crosstalk error: signal separation as a measure of the performance indicators.
一般来说，盲信号分离包括盲信号并行分离和盲信号提取两种方法。
Generally, classical BSS methods include Blind source parallel separation and Blind source extract.
盲信号分离系统可以采集真实语音信号，然后使用不同的盲信号分离算法分离。
Blind signal separation system can capture the true voice signals and use a different algorithm to separate.
近几十年来，盲信号分离因为它在多种问题上的应用潜力受到广大研究者的重视。
Over the past decades, Blind source separation (BSS) has received much research attention because of its potential applicability to many problems.
该文研究超定盲信号分离，即观测信号个数不少于源信号个数情况下的盲信号分离问题。
The problem of overdetermined Blind source Separation (BSS) where there are more mixtures than sources is considered.
研究了盲信号分离的对比函数理论和优化算法理论，着重研究了盲信号提取理论及其算法。
The contrast function theory and the optimal algorithm theory are investigated. The Blind source extract theory and algorithm are also investigated emphatically.
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.
在简化了一般的盲信号分离模型基础上，阐述了该算法的分离准则并对其自适应收敛算法进行了推导。
We analyze the model of separation system, separation criteria and give the deviation of adaptive algorithm of blind signal separation.
实验结果表明，优化后的盲信号分离算法能够在一定程度上降低算法的运算量，并能得到很好的分离效果。
The experiment shows, the algorithm can considerably reduce the calculation burden and the output speech signals are well separated.
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.
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.
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.
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.
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.
讨论一种非平稳卷积混合声音信号盲分离算法。
This paper discusses a blind separation algorithm of non-stationary convolved sounds mixtures.
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.
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.
用高阶奇数多项式拟合非线性混合函数，建立非线性信号盲分离模型。
The model of nonlinear blind source separation(NBSS) is built which the nonlinear transfer function is simulate by the P-th order polynomial function.
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.
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.
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.
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.
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.
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.
介绍了ICA的原理、含义及EASI算法，并仿真了该算法在语音信号盲分离中的应用。
This paper presents the principle, definition and EASI algorithm of ica, and describes the application of the algorithm in the field of speech signal processing.
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.
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.
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