We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Design a Learnable Self-feedback Ratio-Memory Cellular Nonlinear Network (SRMCNN) for Associative Memory Applications

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)
Jui-Lin Lai ; Dept. of Electron., Nat. United Univ., Miao-Li ; Yuan-Hsin Chen ; Yao-Lien Wang

The self-feedback ratio-memory cellular nonlinear network (SRMCNN) structure with learning capability for associative memory applications is designed and analyzed. The architecture was realized the modified Hebbian learning algorithm in the SRMCNN is proposed. It can learn the exemplar patterns and correctly output the recognized patterns in the associative memory. The weights of B template are generated from the product of the desired output pixel value and the nearest five input neighbouring elements as associative memory for all input exemplar patterns. The learned weights are processed in the ratio with the summation of absolute coefficients on the B template to enhance the feature of pattern. As the results shown that it was learned and recognized 8 incompletely exemplar patterns and output correctly pattern. The structure of the SRMCNN with B template and the modified Hebbian learning algorithm for associative memory are implemented in the 9times9 VLSI circuits for the TSMC 0.25 mum 1P5M CMOS technology. The capability of SRMCNN for the more variant exemplar patterns learning and recognition is greatly improved in the auto-associative memory applications

Published in:

Consumer Electronics, 2006. ISCE '06. 2006 IEEE Tenth International Symposium on

Date of Conference:

0-0 0