Abstract
For more than a decade, prediction error has been one powerful tool to measure the performance of a neural network. In this paper, we extend the technique to a kind of fault tolerant neural network. Consider a neural network to be suffering from multiple-node fault, a formulae similar to that of Generalized Prediction Error has been derived. Hence, the effective number of parameter of such a fault tolerant neural network is obtained. A difficulty in obtaining the mean prediction error is discussed and then a simple procedure for estimation of the prediction error empirically is suggested.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)
Bolt, G.: Fault tolerant in multi-layer Perceptrons. PhD Thesis, University of York, UK (1992)
Cavalieri, S., Mirabella, O.: A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks. Neural Networks 12, 91–106 (1999)
Chiu, C.T., et al.: Modifying training algorithms for improved fault tolerance. In: ICNN 1994, vol. I, pp. 333–338 (1994)
Deodhare, D., Vidyasagar, M., Sathiya Keerthi, S.: Sythesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Transactions on Neural Networks 9(5), 891–900 (1998)
Emmerson, M.D., Damper, R.I.: Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application. IEEE Transactions on Neural Networks 4, 788–793 (1993)
Fontenla-Romero, O., et al.: A measure of fault tolerance for functional networks. Neurocomputing 62, 327–347 (2004)
Kullback, S.: Information Theory and Statistics. Wiley, Chichester (1959)
Leung, C.S., Young, G.H., Sum, J., Kan, W.K.: On the regularization of forgetting recursive least square. IEEE Transactions on Neural Networks 10, 1842–1846 (1999)
Leung, C.S., Sum, J.: A fault tolerant regularizer for RBF networks (in submission)
Moody, J.E.: Note on generalization, regularization, and architecture selection in nonlinear learning systems. In: First IEEE-SP Workshop on Neural Networks for Signal Processing (1991)
Murata, N., Yoshizawa, S., Amari, S.: Network information criterion–Determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks 5(6), 865–872 (1994)
Murray, A.F., Edwards, P.J.: Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training. IEEE Transactions on Neural Networks 5(5), 792–802 (1994)
Schneider, N.C.M.H., Young, E.D.: Maximally fault tolerance neural networks. IEEE Transactions on Neural Networks 3(1), 14–23 (1992)
Phatak, D.S., Koren, I.: Complete and partial fault tolerance of feedforward neural nets. IEEE Transactions on Neural Networks 6, 446–456 (1995)
Phatak, D.S., Tcherner, E.: Synthesis of fault tolerance neural networks. In: Proc. IJCNN 2002, pp. 1475–1480 (2002)
Sequin, C.H., Clay, R.D.: Fault tolerance in feedforward artificial neural networks. Neural Networks 4, 111–141 (1991)
Simon, D., El-Sherief, H.: Fault-tolerance training for optimal interpolative nets. IEEE Transactions on Neural Networks 6, 1531–1535 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sum, J., Leung, Cs., Ho, K. (2006). Prediction Error of a Fault Tolerant Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_58
Download citation
DOI: https://doi.org/10.1007/11893028_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
eBook Packages: Computer ScienceComputer Science (R0)