IEEE Transactions on Neural Networks
Kernel Width Optimization for Faulty RBF Neural Networks with Multi-node Open Fault
Neural Processing Letters
On the selection of weight decay parameter for faulty networks
IEEE Transactions on Neural Networks
Generalization error of faulty MLPs with weight decay regularizer
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
On the objective function and learning algorithm for concurrent open node fault
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In classical training methods for node open fault, we need to consider many potential faulty networks. When the multinode fault situation is considered, the space of potential faulty networks is very large. Hence, the objective function and the corresponding learning algorithm would be computationally complicated. This paper uses the Kullback-Leibler divergence to define an objective function for improving the fault tolerance of radial basis function (RBF) networks. With the assumption that there is a Gaussian distributed noise term in the output data, a regularizer in the objective function is identified. Finally, the corresponding learning algorithm is developed. In our approach, the objective function and the learning algorithm are computationally simple. Compared with some conventional approaches, including weight-decay-based regularizers, our approach has a better fault-tolerant ability. Besides, our empirical study shows that our approach can improve the generalization ability of a fault-free RBF network.