On the Activation Function and Fault Tolerance in Feedforward Neural Networks
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概要
- 論文の詳細を見る
Considering the pattern classification/recognition tasks, the influence of the activation function on fault tolerance property of feedforward neural networks is empirically investigated. The singulation results show that the activation function largely influences the fault tolerance and the generalization property of neural networks. It is found that, neural networks with symmetric sigmoid activition function are largely fault tolerant than the networks with asymmetric sigmoid function. However the close relation between the fault tolerance and the generalization property was not observed and the networks with asymmetric activation function slightly generalize better than the networks with the symmetric activation function. First, the influence of the activation function on fault tolerance property of neural networks is investigated on the XOR problem, then the results are generalized by evaluating the fault tolerance property of different NNs implementing different benchmark problems.
- 社団法人電子情報通信学会の論文
- 1998-01-25
著者
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Ito H
Chiba Univ. Chiba‐shi Jpn
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Hammad Nait
Graduate School Of Science And Technology Chiba University
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Hammad Nait
Graduate School Of Science And Technology Chiba Universkity
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Ito Hideo
Faculty Of Engineering Chiba University
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