Independent component analysis by evolutionary neural networks

Yen-Wei Chen; Xiang-Yan Zeng; Zensho Nakao; Katsumi Yamashita

doi:10.1117/12.382925

14 April 2000 Independent component analysis by evolutionary neural networks

Yen-Wei Chen, Xiang-Yan Zeng, Zensho Nakao, Katsumi Yamashita

Proceedings Volume 3962, Applications of Artificial Neural Networks in Image Processing V; (2000) https://doi.org/10.1117/12.382925
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

Yen-Wei Chen, Xiang-Yan Zeng, Zensho Nakao, and Katsumi Yamashita "Independent component analysis by evolutionary neural networks", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); https://doi.org/10.1117/12.382925

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available