A learning algorithm for fault tolerant feedforward neural networks

IEICE Transactions on Information and Systems

Volumn E80-D, Issue 1, 1997, Pages 21-27

  Hammadi, Nait Charif ^a   Ito, Hideo ^a  

^a CHIBA UNIVERSITY   (Japan)

Author keywords

Essential link; Feedforward neural network; Learning algorithm; Open faults; Relevance of synaptic weights

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; ERROR CORRECTION; FAULT TOLERANT COMPUTER SYSTEMS; LEARNING ALGORITHMS;

SYNAPTIC WEIGHTS; TAYLOR EXPANSION;

FEEDFORWARD NEURAL NETWORKS;

EID: 0030649974     PISSN: 09168532     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. S. Hayken, "Neural Networks, A Comprehensive Foundation," IEEE press, 1994.
2. R.C. Frye, E.A. Rietman, and C.C. Wong, "Backpropagation learning and nonidealities in analog neural network hardware," IEEE Trans. Neural Networks, vol.2, no.l, pp.110-117, Jan. 1991.
3. J. Nijhuis, B. Hofflinger, A. Schaik, and L. Spaanenburg, "Limits to fault-tolerance of a feedforward neural network with learning," Digest FTCS, pp.228-235, June 1990.
4. B.E. Segee and M.J. Carter, "Fault tolerance of pruned multilayer networks," Digest IJCNN, pp.II-447-452, 1991.
5. J.I. Minnix, "Fault tolerance of backpropagation neural network trained on noisy inputs," Digest IJCNN, pp.I847-852, 1992.
6. A.F. Murray and P.J. Edwards, "Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training," IEEE Trans. Neural Networks, vol.5, no.5, pp.792-802, 1994.
7. H. Ito and T. Yagi, "Fault tolerant design using error correcting code for multilayer neural networks," IEEE Int. Workshop on Defect and Fault Tolerance in VLSI systems, pp. 177-184, 1994.
8. M.D. Emmerson and R.I. Damper, "Determining and improving the fault tolerance of multilayer perceptions in a pattern-recognition application," IEEE Trans. Neural Networks, vol.4, no.5, pp.788-793, 1993.
9. Y. Tan and T. Nanya, "Fault-tolerant back-propagation model and its generalization ability," Digest IJCNN, pp.2516-2519, 1993.
10. G.P. Drago and S. Ridella, "Statistically controlled activation weight initialization (SCAWI)," IEEE Trans. Neural Networks, vol.3, no.4, pp.627-63I, July 1992.
11. R. Reed, "Pruning algorithms-A survey," IEEE Trans. Neural Networks, vol.4, no.5, pp.740-747, Sept. 1993.
12. D.S. Phatak and I. Koren, "Complete and partial fault tolerance of feedforward neural nets," IEEE Trans. Neural Networks, vol.6, no.2, pp.446-456, March 1995.
13. C.T. Chiu, K. Mehrotra, C.K. Mohan, and S. Ranka, "Training techniques to obtain fault-tolerant neural networks," Digest FTCS, pp.360-369, June 1994.
14. G. Bolt, "Fault models for artificial neural networks," Digest IJCNN, pp.1373-1378, 1991.
15. N.C. Hammadi and H. Ito, "A learning algorithm for fault tolerant feedforward neural networks," IEICE Technical Report, FTS95-78, Feb. 1996.
16. S.B. Holden and P.J.W. Rayner, "Generalization and PAC learning: Some new results for the class of generalized single-layer networks," IEEE Trans. Neural Networks, voL6, no.2, pp.468-480, March 1995.
17. L. Holmstrom and P. Koistinen, "Using additive noise in back-propagation training," IEEE Trans. Neural Networks, vol.3, no.l, pp.24-38, Jan. 1992.
18. J.S.N. Jean and J. Wang, "Weight smoothing to improve network generalization," IEEE Trans. Neural Networks, vol.5, no.5, pp.468-480, Sept. 1995.