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非しきい値型活性化関数を持つ神経回路網群による生理活性推定法
http://hdl.handle.net/10458/3575
http://hdl.handle.net/10458/357552cef496-41e0-4fac-8328-c643fa823aba
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
41engineering40_pp.239-244.pdf (793.7 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2012-03-06 | |||||
| タイトル | ||||||
| タイトル | 非しきい値型活性化関数を持つ神経回路網群による生理活性推定法 | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | Physiological activity estimation using multi-modal neural network with non-threshold activation function | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | physiological activity, protein expression levels, multi-modal neural network, non-threshold activation function | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | departmental bulletin paper | |||||
| その他（別言語等）のタイトル | ||||||
| その他のタイトル | ヒシキイチガタ カッセイカ カンスウ オ モツ シンケイ カイロモウグン ニヨル セイリ カッセイ スイテイホウ | |||||
| 言語 | ja-Kana | |||||
| 著者 |
近藤, 和弘
× 近藤, 和弘× 山森, 一人× 吉原, 郁夫× 近藤, 千博 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Many people expect the third function of foods called as physiological activities that affect our health condition. However, measurement of a physiological activity is troublesome and measurements for all kinds of foods are not impractical. Therefore a system which can easily estimate physiological activities is required. We have proposed a method to estimate physiological activities of foods from protein expression levels using artificial neural networks (ANNs). Estimation of physiological activity using conventional ANN has problems that physiological activities take positive real number more than 1.0, but dynamic range of ANN is limited from 0.0 to 1.0. And estimation accuracy was not enough. To solve those problems, we employ multi-modal neural network with non-threshold activation function. By using multi-modal neural network with non-threshold activation function, ANN can directly handle physiological activities as the training signals those are more than 1.0. Experimental results showed that our method improved estimation accuracy than that of conventional ANN with scaled training samples for some physiological activities. | |||||
| 言語 | en | |||||
| 書誌情報 |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 40, p. 239-244, 発行日 2011-07-30 |
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| 出版者 | ||||||
| 出版者 | 宮崎大学工学部 | |||||
| 言語 | ja | |||||
| 出版者 | ||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||
| 言語 | en | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 05404924 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA00732558 | |||||
| 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 | |||||
