用于多非线性约束混合模型的非线性信号分离
Nonlinear signal separation for multinonlinearity constrained mixing model.
作者信息
Gao P, Woo W L, Dlay S S
出版信息
IEEE Trans Neural Netw. 2006 May;17(3):796-802. doi: 10.1109/TNN.2006.873288.
PMID:16722182
Abstract
In this letter, a new type of nonlinear mixture is derived and developed into a multinonlinearity constrained mixing model. The proposed signal separation solution integrates the Theory of Series Reversion with a polynomial neural network whereby the hidden neurons are spanned by a set of mutually reversed activation functions. Simulations have been undertaken to support the theory of the proposed scheme and the results indicate promising performance.
摘要
在这封信中，推导了一种新型非线性混合，并将其发展为多非线性约束混合模型。所提出的信号分离解决方案将级数反演理论与多项式神经网络相结合，其中隐藏神经元由一组相互反演的激活函数生成。已进行仿真以支持所提方案的理论，结果表明其性能良好。
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