High performance hybrid-ICA to increase convergence speed and accuracy with use of RBF network
High performance hybrid-ICA to increase convergence speed and accuracy with use of RBF network
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DOI：
10.3233/kes-2006-10505
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
E. Uchino;T. Azetsu;N. Suetake
中科院分区：
文献类型：
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作者：
E. Uchino;T. Azetsu;N. Suetake
本文首次提出使用径向基函数(RBF)网络来提高盲信号分离(BSS)的分离性能。独立分量分析(ICA)通常用于盲源分离问题，但通常采用Sigmoid函数来描述信号的概率分布，更准确地说是信号的对数概率密度函数(PDF)的导数。为了提高盲源分离的信号分离性能，我们尝试用径向基函数神经网络来尽可能准确地描述这种非线性导数。我们进一步提出了一种混合ICA，以充分利用传统ICA和基于径向基函数的ICA的优点。将该方法应用于几个信号分离问题。仿真实验验证了该方法的有效性。
In this paper we first propose to use a radial basis function (RBF) network to increase the separation performance of blind signal separation (BSS). The independent component analysis (ICA) is often used for the BSS problem, but in general, the ICA employs the sigmoid function to describe the probability distribution of signal, more precisely the derivative of the logarithmic probability density function (PDF) of signal. In order to enhance the signal separation performance of BSS, we try to describe this nonlinear derivative function as accurately as possible by using RBF network. We further propose a hybrid ICA to make the most of the both advantages of the conventional ICA and the RBF based ICA. The proposed method is applied to several signal separation problems. The effectiveness of the proposed method has been confirmed by simulation experiments.