By Topic

Fault tolerance in neural networks: theoretical analysis and simulation results

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Piuri, V. ; Dept. of Electron., Politecnico di Milano, Italy ; Sami, M. ; Stefanelli, R.

Work is continuing on the intrinsic capacity of survival of fault characterizing neural nets per se. The authors deal with this theme, considering in particular multilayered feedforward nets. The study is performed on the abstract neural graphs, thus involving errors rather than faults. After an initial analysis of the error model, the effects of errors are mathematically derived and the conditions allowing the complete recovery from faults through redistribution of weights in the network (or otherwise allowing predetermined upper bounds on errors) are derived. Simulation results are presented identifying the effect of such errors on the neural computation. It is seen that (unless a good measure of redundancy is present in the net from the beginning) even single errors affect in a relevant way the computation. Correction of this effect is sought through repeated learning, i.e. an operation leading to the weight adjustment previously discussed in theoretical terms

Published in:

CompEuro '91. Advanced Computer Technology, Reliable Systems and Applications. 5th Annual European Computer Conference. Proceedings.

Date of Conference:

13-16 May 1991