Abstract
This paper studies the performance of faulty RBF networks when stuck-at-zero node fault and stuck-at-one node fault happen. An objective function for training fault tolerant RBF networks for node fault is first derived. A training learning algorithm for faulty RBF networks is then presented. Finally, a mean prediction error formula which can estimate the test set error of faulty networks is derived. Simulation experiments are then performed to verify our theoretical result.
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© 2012 Springer-Verlag Berlin Heidelberg
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Leung, C.S., Sum, P.F., Ng, KT. (2012). On the Objective Function and Learning Algorithm for Concurrent Open Node Fault. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_26
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DOI: https://doi.org/10.1007/978-3-642-34487-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
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