Abstract
AbstractThis paper first derives the training objective function of faulty radial basis function (RBF) networks, in which open weight fault and multiplicative weight noise co-exist. A regularizer is then identified from the objective function. Finally, the corresponding learning algorithm is developed. Compared to the conventional approach, our approach has a better fault tolerant ability. We then develop a faulty mean prediction error (FMPE) formula to estimate the generalization ability of faulty RBF networks. The FMPE formula helps us to understand the generalization ability of faulty networks without using a test set or generating a number of potential faulty networks. We then demonstrate how to use our FMPE formula to optimize the RBF width for the co-existing fault situation.KeywordsRBF networksfault tolerance
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