Manipulation of hidden units activities for fault tolerant multi-layer neural networks
説明
We propose a new training algorithm to enhance fault tolerance of multi-layer neural networks (MLNs). This method is based on the fact that faults on connections between hidden layer and output layer have a harmful effect on fault tolerance of MLNs. to decrease these effects, we introduced two approaches, (1) reduce the number of strong connections between hidden layer and output layer, (2) neutralize the activities of hidden units. The first approach aims to reduce the undesirable connections. The second one aims to increase redundancy of internal representation.
収録刊行物
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- Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694)
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Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694) 1 19-24, 2004-03-02
IEEE
