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No. 59 (2000/03) >

Title :Independent Component Analysis by Evolutionary Neural Networks
Authors :Zeng, Xiang-Yan
Chen, Yen-Wei
Nakao, Zensho
Yamashita, Katsumi
Authors alternative :陳, 延偉
仲尾, 善勝
山下, 勝己
Issue Date :Mar-2000
Abstract :In this paper, we propose an evolutionary neural network for blind source separation (BSS). The BSS is the problem to obtain the independent components of original source signals from mixed signals. The original sources that are mutually independent and are mixed linearly by an unknown matrix are retrieved by a separating procedure based on Independent Component Analysis (ICA). The goal of ICA is to find a separating matrix so that the separated signals are as independent as possible. In neural realizations, separating matrix is represented as connection weights of networks and usually updated by learning formulae. The effectiveness of the algorithms, however, is affected by the neuron activation functions that depend on the probability distribution of the signals. In our method, the network is evolved by Genetic Algorithm (GA) that does not need activation functions and works on evolutionary mechanism. The kurtosis that is a simple and original criterion for independence is used as the fitness function of GA. After learning, the network can be used to separate additional signals different from training set but mixed by the same matrix. The applicability of the proposed method for blind source separation is demonstrated by the simulation results.
Type Local :紀要論文
ISSN :0389-102X
Publisher :琉球大学工学部
URI :http://hdl.handle.net/20.500.12000/1959
Citation :琉球大学工学部紀要 no.59 p.79 -85
Appears in Collections:No. 59 (2000/03)

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