Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. For technical support, please contact us at onlinesupport@ieee.org. We apologize for any inconvenience.
By Topic

On improvement in fault tolerance of Hopfield neural networks

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)
Kamiura, N. ; Dept. of Electr. Eng. & Comput. Sci., Hyogo Univ., Himeji, Japan ; Isokawa, T. ; Matsui, N.

Hopfield neural networks tolerating weight faults are presented. The network training is made on condition some faults occur. Statuses of such faults are evoked by intentionally injecting faults into the network. The learning using the single-fault injection is shown first. Learning schemes, which are based on the double-fault injection for a couple of weights within a neuron, are then proposed to improve the fault tolerance further. Experimental results show that the learning using the random-double-fault injection allows us to complete the reasonably dependable network with the acceptable length of the learning time. In addition, the proposed schemes make the network robust against the input noise.

Published in:

Test Symposium, 2004. 13th Asian

Date of Conference:

15-17 Nov. 2004