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

by Amit Prakash Singh, Pravin Chandra, Chandra Shekhar Rai
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 2
Year of Publication: 2010
Authors: Amit Prakash Singh, Pravin Chandra, Chandra Shekhar Rai
10.5120/627-881

Amit Prakash Singh, Pravin Chandra, Chandra Shekhar Rai . Empirical Study of Least Sensitive FFANN for Weight-Stuck-at Zero Fault. International Journal of Computer Applications. 2, 2 ( May 2010), 47-51. DOI=10.5120/627-881

@article{ 10.5120/627-881,
author = { Amit Prakash Singh, Pravin Chandra, Chandra Shekhar Rai },
title = { Empirical Study of Least Sensitive FFANN for Weight-Stuck-at Zero Fault },
journal = { International Journal of Computer Applications },
issue_date = { May 2010 },
volume = { 2 },
number = { 2 },
month = { May },
year = { 2010 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume2/number2/627-881/ },
doi = { 10.5120/627-881 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:49:42.529185+05:30
%A Amit Prakash Singh
%A Pravin Chandra
%A Chandra Shekhar Rai
%T Empirical Study of Least Sensitive FFANN for Weight-Stuck-at Zero Fault
%J International Journal of Computer Applications
%@ 0975-8887
%V 2
%N 2
%P 47-51
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Fault models Sensitivity analysis

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