Empirical Study of FFANN Tolerance to Weight Stuck at Max/Min Fault
Amit Prakash Singh, Chandra Shekhar, Pravin Chandra · International Journal of Artificial Intelligence & Applications · 2010
Abstract — Fault tolerance property of artificial neural networks has been investigated with reference to the hardware model of artificial neural networks. Weight fault is an important link, which causes breakup between two nodes. In this paper weight fault has been explained. Experiments have been performed for Weight-stuck-0 fault. Effect of weight-stuck-0 fault on trained network has been analyzed in this paper. The obtained results suggest that networks are not fault tolerant to this type of fault. Keywords- artificial neural network,weight Fault, fault tolerance I.