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Signal classification based on frequency analysis using multilayer neural network with limited data and computations
http://hdl.handle.net/2297/18398
http://hdl.handle.net/2297/183983bf3e49a-c96a-4a24-b926-70457fd67eab
名前 / ファイル | ライセンス | アクション |
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TE-PR-NAKAYAMA-K-600.pdf (455.3 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2017-10-03 | |||||
タイトル | ||||||
タイトル | Signal classification based on frequency analysis using multilayer neural network with limited data and computations | |||||
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 |
Hara, Kazuyuki
× Hara, Kazuyuki× Nakayama, Kenji |
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提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学理工研究域 電子情報学系 | |||||
書誌情報 |
IEEE International Conference on Neural Networks - Conference Proceedings 巻 1, p. 600-605, 発行日 1995-11-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1098-7576 | |||||
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出版者 | IEEE = Institute of Electrical and Electronics Engineers | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Signal classification performance using multilayer neural network (MLNN) and the conventional signal processing methods are theoretically compared under the limited observation period and computational load. The signals with N samples are classified based on frequency components. The comparison is carried out based on degree of freedom the signal detection regions in an N-dimensional signal space. As a result, the MLNN has higher degree of freedom, and can provide more flexible performance for classifying the signals than the conventional methods. This analysis is further investigated throught computer simulations. Multi-frequency signals and the real application, a dial tone receiver, are taken into account. As a result, the MLNN can provide much higher accuracy than the conventional signal processing methods. | |||||
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出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |