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Proceedings Paper

Independent component analysis by evolutionary neural networks
Author(s): Yen-Wei Chen; Xiang-Yan Zeng; Zensho Nakao; Katsumi Yamashita
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Paper 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 in the fitness function of GA. After learning, the network can be used to separate other mixed signals of the same mixing procedure. The applicability of the proposed method for blind source separation is demonstrated by the simulation results.

Paper Details

Date Published: 14 April 2000
PDF: 8 pages
Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); doi: 10.1117/12.382925
Show Author Affiliations
Yen-Wei Chen, Univ. of the Ryukyus (Japan)
Xiang-Yan Zeng, Univ. of the Ryukyus (Japan)
Zensho Nakao, Univ. of the Ryukyus (Japan)
Katsumi Yamashita, Univ. of the Ryukyus (Japan)


Published in SPIE Proceedings Vol. 3962:
Applications of Artificial Neural Networks in Image Processing V
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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