COMPARISON OF TWO TYPICAL FAULT-TOLERANCE ALGORITHMS OF NEURAL NETWORKS
两种典型神经网络容错方法的比较

There are two typical fault tolerance algorithms of feed forward neural networks. One is SC algorithm presented by Behnam and the other is rehidden algorithm presented by this paper. The former modifies the BP algorithm in the training phase to gain fault tolerant network, and the latter gives some redundant nodes to the hidden layer of the trained neural network. There are advantage and shortage in both algorithms. We do simulations on these two algorithms. Analysis of the simulation results show that either algorithm has its own applicable network scale and hardware condition. In different condition, different algorithm should be used to gain suitable fault tolerant neural network. Finally, we also give some analysis to some improvement of SC algorithm.