Presentation | 2000/5/11 Analysis on inhabitant's reaction to environmental noise via regularized neural nets Katsuyuki HAGIWARA, Tetsuya YAMAMOTO, Yuichi NORO, Kazuhiro KUNO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this article, we applied layered neural nets with regularization method to predict and analyze inhabitant's reaction to environmental noise. In the work, the inhabitant's reaction was predicted and analyzed at commercial areas, industrial areas and residential areas, respectively. As a result, firstly, the prediction capability of neural nets with regularization is found to be superior to neural nets without regularization at any areas. Secondly, the analysis through the trained neural nets with regularization revealed that the prediction is essentially influenced by the volume of traffic at daytime in all areas. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | environmental noise / inhabitant's reaction / layered neural nets / regularization |
Paper # | EA2000-7 |
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Committee | EA |
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Conference Date | 2000/5/11(1days) |
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Registration To | Engineering Acoustics (EA) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Analysis on inhabitant's reaction to environmental noise via regularized neural nets |
Sub Title (in English) | |
Keyword(1) | environmental noise |
Keyword(2) | inhabitant's reaction |
Keyword(3) | layered neural nets |
Keyword(4) | regularization |
1st Author's Name | Katsuyuki HAGIWARA |
1st Author's Affiliation | Faculty of Physics Engineering, Mie University() |
2nd Author's Name | Tetsuya YAMAMOTO |
2nd Author's Affiliation | Faculty of Physics Engineering, Mie University |
3rd Author's Name | Yuichi NORO |
3rd Author's Affiliation | Faculty of Physics Engineering, Mie University |
4th Author's Name | Kazuhiro KUNO |
4th Author's Affiliation | Faculty of Physics Engineering, Mie University |
Date | 2000/5/11 |
Paper # | EA2000-7 |
Volume (vol) | vol.100 |
Number (no) | 53 |
Page | pp.pp.- |
#Pages | 8 |
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