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An incremental self-organizing neural network based on enhanced competitive Hebbian learning

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タイトル: An incremental self-organizing neural network based on enhanced competitive Hebbian learning
著者: Liu, Hao 著作を一覧する
Kurihara, Masahito 著作を一覧する
Oyama, Satoshi 著作を一覧する
Sato, Haruhiko 著作を一覧する
発行日: 2013年
出版者: IEEE (Institute of Electrical and Electronics Engineers)
引用: The 2013 International Joint Conference on Neural Networks (IJCNN), ISBN: 978-1-4673-6129-3
開始ページ: 1
終了ページ: 8
出版社 DOI: 10.1109/IJCNN.2013.6706725
抄録: Self-organizing neural networks are important tools for realizing unsupervised learning. Recently, a difficult task has involved the incremental, efficient and robust learning in noisy environments. Most of the existing techniques are poor in this regard. In this paper, we first propose a new topology generating method called enhanced competitive Hebbian learning (enhanced CHL), and then propose a novel incremental self-organizing neural network based on the enhanced CHL method, called enhanced incremental growing neural gas (Hi- GNG). The experiments presented in this paper show that the Hi-GNG algorithm can automatically and efficiently generate a topological structure with a suitable number of neurons and that the proposed algorithm is robust to noisy data.
Rights: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
資料タイプ: proceedings (author version)
URI: http://hdl.handle.net/2115/65582
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 小山 聡

 

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