Influence on ANNs fault tolerance of binary errors introduced during training

Author

Assoum, Ammar ; Geagea, Maïkel ; Velazco, Raoul

Author_Institution

Fac. of Sci. III, Lebanese Univ., Tripoli, Lebanon

fYear

2004

fDate

19-23 April 2004

Firstpage

435

Lastpage

436

Abstract

This paper presents the effect of binary errors on artificial neural networks during the training phase and the fault tolerance of ANN. The tested network implements a problem (recognition of digits and letters) and is trained in the presence of binary errors. A significant improvement is obtained with increase in the number of perturbations during the training phase and recognition rate during the generalization phase.

Keywords

fault tolerance; generalisation (artificial intelligence); learning (artificial intelligence); neural nets; perturbation techniques; artificial neural network; binary error; digit recognition; fault tolerance; generalization phase; network test; perturbation; recognition rate; training phase; Application software; Artificial neural networks; Circuit simulation; Fault tolerance; Laboratories; Neural network hardware; Neural networks; Neurons; Single event upset; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies: From Theory to Applications, 2004. Proceedings. 2004 International Conference on

Print_ISBN

0-7803-8482-2

Type

conf

DOI

10.1109/ICTTA.2004.1307818

Filename

1307818

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3101020