Signal extensions in independent component analysis and its application for real-time processing
Signal extensions in independent component analysis and its application for real-time processing
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DOI：
10.1109/cit.2004.1357299
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发表时间：
2004-09
期刊：
影响因子：
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通讯作者：
Shuxue Ding;Jie Huang;D. Wei;S. Omata
中科院分区：
文献类型：
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作者：
Shuxue Ding;Jie Huang;D. Wei;S. Omata
在本文中，我们基于同时扰动随机逼近（SPSA）的梯度学习，研究了与独立成分分析（ICA）实时处理相关的一些问题。实时 ICA 处理对于动态混合环境中的应用尤其必要，因为批处理类型的 ICA 处理只能在静态或固定混合环境中正常工作。尽管可以应用 SPSA 的 ICA 目标函数有很多选择，但在本文中，我们选择非线性相关矩阵的对角线作为我们的目标函数。描述了该算法的理论和实现。还提供了计算机模拟的结果来证明其有效性。
In this paper, we investigate some issues related to realtime processing for independent component analysis (ICA), based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). Real-time ICA processing is especially necessary for an application in dynamic mixing environment, since a batch type of ICA processing can work well only in a static or stationary mixing environment. Although there are many choices for an ICA object function to which SPSA can be applied, in this paper, we choose a diagonality of the nonlinear correlation matrix as our object function. Theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness.