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  1. 紀要論文
  2. 琉球大学工学部紀要
  3. 59号
  1. 部局別インデックス
  2. 工学部

Independent Component Analysis by Evolutionary Neural Networks

http://hdl.handle.net/20.500.12000/1959
http://hdl.handle.net/20.500.12000/1959
0e6efe0d-553e-400f-a371-9dade24daccb
名前 / ファイル ライセンス アクション
No59p79.pdf No59p79.pdf
Item type デフォルトアイテムタイプ(フル)(1)
公開日 2007-09-16
タイトル
タイトル Independent Component Analysis by Evolutionary Neural Networks
言語 en
作成者 Zeng, Xiang-Yan

× Zeng, Xiang-Yan

en Zeng, Xiang-Yan

Chen, Yen-Wei

× Chen, Yen-Wei

en Chen, Yen-Wei

Nakao, Zensho

× Nakao, Zensho

en Nakao, Zensho

Yamashita, Katsumi

× Yamashita, Katsumi

en Yamashita, Katsumi

陳, 延偉

× 陳, 延偉

ja 陳, 延偉

仲尾, 善勝

× 仲尾, 善勝

ja 仲尾, 善勝

山下, 勝己

× 山下, 勝己

ja 山下, 勝己

アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
主題
言語 en
主題Scheme Other
主題 Blind source separation
言語 en
主題Scheme Other
主題 Independent Component Analysis
言語 en
主題Scheme Other
主題 Genetic Algorithm
言語 en
主題Scheme Other
主題 Kurtosis
内容記述
内容記述タイプ Other
内容記述 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.
内容記述タイプ Other
内容記述 紀要論文
出版者
言語 ja
出版者 琉球大学工学部
言語
言語 eng
資源タイプ
資源タイプ departmental bulletin paper
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
識別子
識別子 http://hdl.handle.net/20.500.12000/1959
識別子タイプ HDL
収録物識別子
収録物識別子タイプ ISSN
収録物識別子 0389-102X
収録物識別子タイプ NCID
収録物識別子 AN0025048X
収録物名
言語 ja
収録物名 琉球大学工学部紀要
書誌情報
号 59, p. 79-85
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