Name
Abstract | ||
|---|---|---|
Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states;
however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In
this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems.
It is based on the Alpha-Beta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall
of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented.
|
Year | DOI | Venue |
|---|---|---|
2007 | 10.4304/jcp.2.4.49-56 | Journal of Computers |
Keywords | Field | DocType |
index terms— bidirectional associative memories,former model,noisy pattern,stability problem,correct recall,new model,fingerprint recognition,alpha-beta bidirectional associative memory,bidirectional associative memory,perfect recall,perfect recall.,alpha- beta associative memories,alpha-beta associative memory,trained pattern,associative memory | Content-addressable memory,Associative property,Pattern recognition,Iterative method,Bidirectional associative memory,Computer science,Fingerprint recognition,Artificial intelligence,Artificial neural network,Ambiguity,Recall,Distributed computing | Journal |
Volume | Issue | ISSN |
2 | 4 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-47242-8 | 3 | 0.44 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Elena Acevedo-Mosqueda | 1 | 20 | 2.63 |
| Cornelio Yáñez-Márquez | 2 | 153 | 26.34 |
| Itzamá López-Yáñez | 3 | 78 | 11.76 |