Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Comparative Fault Tolerance of Parallel Distributed Processing Networks
IEEE Transactions on Computers
A Modified Backpropagation Algorithm to Tolerate Weight Errors
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Complete and partial fault tolerance of feedforward neural nets
IEEE Transactions on Neural Networks
Obtaining Fault Tolerant Multilayer Perceptrons Using an Explicit Regularization
Neural Processing Letters
Assessing the Noise Immunity and Generalization of Radial Basis Function Networks
Neural Processing Letters
Analysis on Bidirectional Associative Memories with Multiplicative Weight Noise
Neural Information Processing
IEEE Transactions on Neural Networks
On the selection of weight decay parameter for faulty networks
IEEE Transactions on Neural Networks
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The inherent fault tolerance of artificial neuralnetworks (ANNs) is usually assumed, but severalauthors have claimed that ANNs are not always faulttolerant and have demonstrated the need to evaluatetheir robustness by quantitative measures. For thispurpose, various alternatives have been proposed. Inthis paper we show the direct relation between themean square error (MSE) and the statisticalsensitivity to weight deviations, defining a measureof tolerance based on statistical sentitivity that wehave called Mean Square Sensitivity (MSS); this allowsus to predict accurately the degradation of the MSEwhen the weight values change and so constitutes auseful parameter for choosing between differentconfigurations of MLPs. The experimental resultsobtained for different MLPs are shown and demonstratethe validity of our model.