Abstract
This paper presents a technique for improving the fault tolerance capability of Artificial Neural Networks. This characteristic of distributed systems, which is usually pointed out as one of the advantages of this structure hasn’t been deeply studied and can be improved in most of the networks. The solution implemented here consists of changing the architecture of feedforward artificial neural networks after the training stage while maintaining its output unchanged. It involves evaluating the elements of the Artificial Neural Network which are more sensible to a fault and duplicating inputs, bias, weights or neurons, according to the evaluation done before. This solution is very interesting because it allows maintaining the pre-trained network, but its cost is the need of additional hardware resources to implement the same network. The paper also presents an example of the application of the technique to illustrate its effectiveness.
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© 2008 Springer-Verlag Berlin Heidelberg
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Dias, F.M., Antunes, A. (2008). Fault Tolerance Improvement through Architecture Change in Artificial Neural Networks. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_28
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DOI: https://doi.org/10.1007/978-3-540-92137-0_28
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