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New Metrics for Sensitivity Analysis of FFANN

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

Journal: International Journal of Engineering Science and Technology
ISSN 0975-5462

Volume: 2;
Issue: 4;
Start page: 322;
Date: 2010;
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Keywords: Artificial Neural Network | Weight Fault | Sensitivity Metrics | Fault-Tolerance

An important issue in the design of a neural network is the sensitivity of its output to input and weight faults. In this paper, new fault metrics for sensitivity analysis is derived. The experimental derivation of the derived fault metrics is demonstrate that upper bound envelop exist with empirical error of the neural network. Correlation coefficient between MEANWSZ and derived sensitivity analysis for each problem concludes that derived fault metric gives the better results.
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