Prediction error of a fault tolerant neural network

Authors:
John Sum;Chi-sing Leung;Kevin Ho
Affiliations:
Department of Information Management, Chung Shan Medical University, Taichung, Taiwan;Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, KLN, Hong Kong;Department of Computer Science and Communication Engineering, Providence University, Sha-Lu, Taiwan
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 5
Cited 0

A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks

Neural Networks
Synthesis of fault-tolerant feedforward neural networks using minimax optimization

IEEE Transactions on Neural Networks
On the regularization of forgetting recursive least square

IEEE Transactions on Neural Networks
Complete and partial fault tolerance of feedforward neural nets

IEEE Transactions on Neural Networks
Fault-tolerant training for optimal interpolative nets

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.