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A structure trainable neural network with embedded gate units: Multi-dimensional input/output and its learning algorithm
http://hdl.handle.net/2297/6826
http://hdl.handle.net/2297/68263c7e4cf6-bf47-4d01-a596-e162cefd4e3d
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-1681.pdf (484.3 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | A structure trainable neural network with embedded gate units: Multi-dimensional input/output and its learning algorithm | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Nakayama, Kenji
× Nakayama, Kenji× Hirano, Akihiro× Kourin, Makoto |
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| 書誌情報 |
Proceedings of the International Joint Conference on Neural Networks 巻 3, p. 1681-1686, 発行日 2001-07-01 |
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| 出版者 | ||||||
| 出版者 | IEEE(Institute of Electrical and Electronics Engineers) | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this paper, a synthesis and learning method for the neural network with embedded gate units and a multi-dimensional input is proposed. When the input is multi-dimensional, gate functions are controlled in a multi-dimensional space. In this case, a hypersurface, on which the gate function is formed should be optimized. Furthermore, the switching points should be considered on the unit input. An initialization and a control methods for gate functions, which optimize the hypersurface, the switching point and the inclination, are proposed. The stabilization methods, already proposed, are further modified to be applied to the multi-dimensional environment. The gate functions can be trained together with the connection weights. Discontinuous function approximation is demonstrated to confirm usefulness of the proposed method. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
