Obtaining Fault Tolerant Multilayer Perceptrons Using an Explicit Regularization
Neural Processing Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximation to the performance degradation of a MLP affected by perturbations. In the present paper, the expression of MSS for Radial Basis Function Neural Networks affected by additive noise in their inputs is obtained. This expression is experimentally validated, allowing us to propose MSS as an effective measurement of noise immunity of RBFNs.