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Blind separation of dependent sources using Schweizer-Wolff measure

机译:使用Schweizer-Wolff测量盲目的依赖来源分离

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There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.
机译:有许多应用程序需要考虑通常表现光或强大依赖的来源,这不是常见盲信号分离(BSS)算法可以做的情况。本文的目的是开发用于线性相关源信号的非参数BSS算法,这是在对比度方法的框架下提出的。对比度函数源自来自变量之间的成对依赖性的Schweizer-Wolff测量。仿真结果表明,该算法能够分离依赖信号并产生理想的性能。

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