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Empirical Study of Least Sensitive FFANN for Weight- Stuck-at Zero Fault

Author(s): Amit Prakash Singh | Pravin Chandra | Chandra Shekhar Rai

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 2;
Issue: 2;
Start page: 47;
Date: 2010;
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Keywords: Artificial Neural Network | Fault models | Sensitivity analysis.

An important consideration for neural hardware is its sensitivityto input and weight errors. In this paper, an empirical study isperformed to analyze the sensitivity of feedforward neuralnetworks for Gaussian noise to input and weight. 30 numbers ofFFANN is taken for four different classification tasks. Leastsensitive network for input and weight error is chosen for furtherstudy of fault tolerant behavior of FFANN. Weight stuck-at zerofault is selected to study error metrics of fault tolerance.Empirical results for a WSZ fault is demonstrated in this paper.
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