Water Bottom Material Discrimination Using Multilayered Neural Network
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概要
- 論文の詳細を見る
The measurement to establish the discrimination method of water bottom material using the pulse echo method and neural network data processing were performed in a model water bath. Furthermore, the optimum learning and discriminating conditions were studied in order to obtain a good discrimination result by restraining the changes in intensity level of the echo signals. It is concluded that optimum values of learning and discriminating data to be input to the multilayered neural network are the mean value of 6 datums for learning data and 20 mean values of 3 datums each for discriminating data.
- 社団法人応用物理学会の論文
- 1994-05-30
著者
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TAKANO Nobuyuki
Department of Electronics, Chiba Institute of Technology
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MOTOOKA Seiichi
Department of Electronics, Chiba Institute of Technology
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Takano N
Waseda Univ. Tokyo Jpn
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Motooka Seiichi
Department Of Electronics Chiba Institute Of Technology
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Motooka Seiichi
Department Of Electronics
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KOUNO Toshiyoshi
Department of Electronics, Chiba Institute of Technology
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Kouno T
Kougakuin Univ.
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Motooka Seiichi
Department of Electrical, Electronics and Computer Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan
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