By Topic

Inherent fault tolerance analysis for a class of multi-layer neural networks with weight deviations

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

2 Author(s)
Xiaofan Yang ; Comput. Inst., Chongqing Univ., Sichuan, China ; Tinghuai Chen

The general formula of computing the deviation of the output of a multilayer neural network (MLNN) with respect to the deviations of its input and of its weights is presented. The upper bound of the deviation propagation from level to level is well estimated with certain probability. Based on this, one can analyze the relation between the topological structure of an MLNN and its fault tolerance property, which can be used to correctly design fault tolerant MLNNs

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference:

1993