Referenced Items are not available for this document.
A high-gain, post-annealing, generalized Hebbian algorithm is proposed and observed to have chaotic learning behavior. It lends itself readily to a highly efficient parallel distributed architecture for principal components computation. The work is extended to a convergence accelerator that uses the chaotic pattern learned during the first few epochs for an iterative weight-change procedure. Applications of using the parallel architecture for image encoding, reconstruction, and matching are described. Successful simulation results in yielding good quality reconstructed images and photo/sketch matching are reported
Published in:
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
(Volume:1
)
Date of Conference: 2002
- Page(s):
- 974 - 979
- Meeting Date :
- 12 May 2002-17 May 2002
- ISSN :
- 1098-7576
- Print ISBN:
- 0-7803-7278-6
- INSPEC Accession Number:
- 7327748
- Conference Location :
- Honolulu, HI
- Digital Object Identifier :
- 10.1109/IJCNN.2002.1005607
- Product Type:
- Conference Publications
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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
