Modifying training algorithms for improved fault tolerance

Ching Tai Chiu, Kishan G Mehrotra, Chilukuri K. Mohan, Sanjay Ranka

Research output: Chapter in Book/Entry/PoemConference contribution

38 Scopus citations


This paper presents three approaches to improve fault tolerance of neural networks. In two approaches, the traditional backpropagation training algorithm is itself modified so that the trained networks have improved fault tolerance; we achieve better results than others [1, 10] who had also explored this possibility. Our first method is to coerce weights to have low magnitudes, during the training process, since fault tolerance is degraded by the use of high magnitude weights; at the same time, additional hidden nodes are added dynamically to the network to ensure desired performance can be reached. Our second method is to add artificial faults to various components (nodes and links) of a network during training. This leads to the development of networks that perform well even when faults occur in the network. The third method repeatedly eliminates nodes of least sensitivity, then 'splits' the most sensitive nodes and retrains the system. This generally results in the best performance, although it requires a small amount of additional retraining after a network is built. Experimental results have shown that these methods can obtain better robustness than backpropagation training, and compare favorably with other approaches [1, 10].

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE Computer Society
Number of pages6
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994


OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA

ASJC Scopus subject areas

  • Software


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