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In neural network training, adding a regularization term into the objective function is an effective method to improve the generalization ability and fault tolerance. Recently, an open node fault regularizer (ONFR) approach was proposed to train radial basis function (RBF) networks. However, this approach only aims at minimizing the training set error of the trained network under the open node fault...
Prediction error is a powerful tool that measures the performance of a neural network. In this paper, we extend the technique to a kind of fault tolerant neural networks. Considering a neural network with multiple-node fault, we derive its generalized prediction error. Hence, the effective number of parameters of such a fault tolerant neural network is obtained. The difficulty in obtaining the mean...
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