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Weight shifting techniques for self-recovery neural networks

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3 Author(s)
C. Khunasaraphan ; Center for Adv. Comput. Studies, Southwestern Louisiana Univ., Lafayette, LA, USA ; K. Vanapipat ; C. Lursinsap

In this paper, a self-recovery technique of feedforward neural networks called weight shifting and its analytical models are proposed. The technique is applied to recover a network when some faulty links and/or neurons occur during the operation. If some input links of a specific neuron are detected faulty, their weights will be shifted to healthy links of the same neuron. On the other hand, if a faulty neuron is encountered, then we can treat it as a special case of faulty links by considering all the output links of that neuron to be faulty. The aim of this technique is to recover the network in a short time without any retraining and hardware repair. We also propose the hardware architecture for implementing this technique

Published in:

IEEE Transactions on Neural Networks  (Volume:5 ,  Issue: 4 )