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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/4701
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| タイトル: | Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms |
| 著者: | SUGAWARA, Eiko
FUKUSHI, Masaru
HORIGUCHI, Susumu |
| キーワード: | self-reconfiguration
multi-layer neural network
weight training by genetic algorithm
FPGA |
| 発行日: | 2004-08-01 |
| 出版者: | 電子情報通信学会 |
| 誌名: | IEICE TRANSACTIONS on Information and Systems |
| 巻: | E87-D |
| 号: | 8 |
| 開始ページ: | 2021 |
| 終了ページ: | 2028 |
| 抄録: | This paper addresses the issue of reconfiguring multi-layer neural networks implemented in single or multiple VLSI chips. The ability to adaptively reconfigure network configuration for a given application, considering the presence of faulty neurons, is a very valuable feature in a large scale neural network. In addition, it has become necessary to achieve systems that can automatically reconfigure a network and acquire optimal weights without any help from host computers. However, self-reconfigurable architectures for neural networks have not been studied sufficiently. In this paper, we propose an architecture for a self-reconfigurable multi-layer neural network employing both reconfiguration with spare neurons and weight training by GAs. This proposal offers the combined advantages of low hardware overhead for adding spare neurons and fast weight training time. To show the possibility of self-reconfigurable neural networks, the prototype system has been implemented on a field programmable gate array. |
| Rights: | Copyright (C)2004 IEICE. Eiko Sugawara, Masaru Fukushi, Susumu Horiguchi, IEICE TRANSACTIONS on Information and Systems, E87-D(8), 2004, 2021-2028. http://www.ieice.org/jpn/trans_online/ |
| URI: | http://hdl.handle.net/10119/4701 |
| 資料タイプ: | publisher |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
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| 4725.pdf | | 597Kb | Adobe PDF | 見る/開く |
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