Cart (Loading....) | Create Account
Close category search window
 

Dynamic associative memory using chaotic 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

2 Author(s)
Fukuhara, Y. ; Graduate Sch. of Media & Gov., Keio Univ., Kanagawa, Japan ; Takefuji, Y.

In this paper, we propose a multimodule chaotic associative memory (MCAM) that uses chaotic neural networks. In this method, the chaotic associative memories are connected to each other. If MCAM can not obtain enough information of a target, MCAM shows a behavior that looks like human “perplexity”, where MCAM succeeds in one-to-many associations. And when MCAM obtains enough information to recognize a target, MCAM converges to a stable state. Although the structure of MCAM is simple, MCAM realizes one-to-many association by using chaotic dynamics

Published in:

Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on  (Volume:2 )

Date of Conference:

1999

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.