Fault tolerant training of neural networks for learning vector quantization

Authors:
Takashi Minohara
Affiliations:
Dept. of Computer Science, Takushoku University, Tokyo, Japan
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
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Abstract

The learning vector quantization(LVQ) is a model of neural networks, and it is used for complex pattern classifications in which typical feedforward networks don't give a good performance. Fault tolerance is an important feature in the neural networks, when they are used for critical application. Many methods for enhancing the fault tolerance of neural networks have been proposed, but most of them are for feedforward networks. There is scarcely any methods for fault tolerance of LVQ neural networks. In this paper, I proposed a dependability measure for the LVQ neural networks, and then I presented two idea, the border emphasis and the encouragement of coupling, to improve the learning algorithm for increasing dependability. The experiment result shows that the proposed algorithm trains networks so that they can achieve high dependability.