An algorithm for real-time independent component analysis in dynamic environments
An algorithm for real-time independent component analysis in dynamic environments
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DOI：
10.1109/mwscas.2004.1354302
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
Shuxue Ding;D. Wei;S. Omata
中科院分区：
文献类型：
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作者：
Shuxue Ding;D. Wei;S. Omata
提出了一种基于梯度学习和同时扰动随机逼近（SPSA）的独立分量分析（伊卡）算法。该算法可以很好地工作在伊卡处理的在线模式下，在一个动态的混合环境。它甚至对于非平稳和/或非同独立分布（non-I.I.D.）信号，因此该算法非常适合于大多数实时应用。本文描述了该算法的原理和实现。计算机仿真结果也证明了这种方法的有效性。
We present a novel algorithm for independent component analysis (ICA) based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). This algorithm can work well in on-line mode of ICA processing, in a dynamic mixing environment. It converges very fast even for non-stationary, and/or non-identically independent distributed (non-I.I.D.) signals, so that the algorithm is very suitable for most real-time applications. In this paper, theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness.