盲源分离的多层广义平均神经元模型

Meenakshi Singh, Deepak Singh, P. Kalra
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引用次数: 3

摘要

盲源分离(BSS)的基本问题是从混合系统的输出中找到一组独立的信号,而不需要混合系统性质的信息,因此大多数BSS算法都使用独立分量分析的概念。本文提出了一种新的独立分量分析(ICA)神经元模型,该模型可用于分离非线性和噪声混合信号。本文提出的技术利用广义均值神经元(GMN)模型,该模型由一个基于所有输入的广义均值的聚合函数组成,该函数用于信号混合。该方法收敛速度快,对欠确定系统效率高,占用CPU时间少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multilayer Generalized Mean Neuron model for Blind Source Separation
The fundamental issue in blind source separation (BSS) is to find a set of independent signals from the output of the mixing system, without the aid of information about the nature of the mixing system, for which most of the BSS algorithms use the concept of Independent component analysis. This paper proposes a new neuron model for independent component analysis (ICA) which can be used for separation of non-linear and noisy mixtures of signals. The technique proposed here utilizes generalized mean neuron (GMN) model, consisting of an aggregation function which is based on the generalized mean of all the inputs applied to signal mixtures. The proposed technique results in faster convergence, and is highly efficient for underdetermined system, with low CPU time.
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