Abstract
This 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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Leung, CS., Sum, J.PF. (2011). Regularizer for Co-existing of Open Weight Fault and Multiplicative Weight Noise. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_30
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DOI: https://doi.org/10.1007/978-3-642-24965-5_30
Publisher Name: Springer, Berlin, Heidelberg
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