Output Smoothing: A learning Algorithm for Fault Tolerant Neural Networks
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概要
- 論文の詳細を見る
The neural networks (NN) are often assumed to be fault tolerant since they contain a large number of computational units. However, experimental evidence is to the contrary. In an attempt to improve the fault tolerance ability of neural networks, one can create different architecture by duplicating the hidden units as in. But, this fault-tolerance comes from the dedicated redundancy added for that purpose, leaving the inherent redundancy of the network unused. To utilize the inherent redundancy of the network a new learning algorithm is proposed which enhances the fault-tolerance of the NN.
- 社団法人電子情報通信学会の論文
- 1997-03-06
著者
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Hammadi Charif
Faculty Of Engineering Chiba University
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Ahmad N
千葉大 工
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ITO Hideo
Faculty of Engineering, Chiba University
-
Ahmad Nasir
Faculty of Engineering, Chiba University
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Ahmad Nasir
Faculty Of Engineering Chiba University
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Ito Hideo
Faculty Of Engineering Chiba University
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