A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS

Xu Liqin; Hu Dongcheng

Volume 23 Issue 10

Oct. 2001

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Xu Liqin, Hu Dongcheng . A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2001, 23(10): 984-988.

Citation:

Xu Liqin, Hu Dongcheng . A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2001, 23(10): 984-988.

Xu Liqin, Hu Dongcheng . A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2001, 23(10): 984-988.

Citation:

Xu Liqin, Hu Dongcheng . A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2001, 23(10): 984-988.

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A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS

Received Date: 1999-09-29
Rev Recd Date: 2000-02-03
Publish Date: 2001-10-19

Abstract

Abstract

There are two main types of approaches in the research of fault tolerance of Multilayer Perceptrons(MLP): improvement in the learning algorithm and component redundancy after training. A dynamic redundancy BP algorithm is presented. In the training steps of the conventional adaptive BP algorithm with a momentum term, the most important weights are replicated based on their significance. Applying the algorithm the fault tolerance of a network can be improved effectively. Compared with fault injection while training--a typical improved learning algorithm, although this dynamic redundancy algorithm gives no prominence in fault tolerance, the training time can be greatly reduced.

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References(1)

References

A.F. Murray, P. J. Edwards, Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training, IEEE Trans. on Neural Networks, 1994, NN-5(5), 792802.[2]P. Kerlinzin, P. Refregier, Theoretical investigation of the robustness of multilayer perceptrons,Analysis of the linear case and extension to nonlinear networks, IEEE Trans. on Neural Networks.1995, NN-6(3), 560-571.[3]B.S. Arad, A. El-Anway, On fault tolerant training of feedforward neural networks, Neural Networks, 1997, 10(3), 539-557.[4]D.S. Phatak, I. Koren, Complete and partial fault tolerance of feedforward neural nets, IEEE Trans. on Neural Networks, 1995, NN-6(2), 446-456.[5]D.S. Phatak, I. Koren, Fault tolerance of feedforward neural nets for classification tasks, IJCNN,Nagoya, Japan, 1992, II-386-391.[6]Xu Liqin.[J].Hu Dongcheng, A new component redundancy method in MLPs fault tolerance, ICEMI99, Harbin, China.1999,:-

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