Generalization and fault tolerance in rule-based neural networks

How to obtain maximum generalization and fault-tolerance has been an important issue in designing a feedforward network. Research on rule-based neural networks suggests that generalization of a neural network is related to the directions of the pattern vectors encoded by hidden units, while fault-tolerance depends on the magnitudes of the weights. In this paper, a rule-based neural network is shown better than a standard neural network both in generalization and fault tolerance. In addition, a formal measure for evaluating network fault tolerance is introduced