Name
Abstract | ||
|---|---|---|
Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25330-0_46 | MICAI (2) |
Keywords | Field | DocType |
non iterative hopfield associative,hopfield associative memory,corrective vector,hopfield model,pattern processing field,efficient pattern,new algorithm,proposed algorithm,hopfield memory,pattern recognition field,autoassociative memory,associative memory,neural networks | Convergence (routing),Autoassociative memory,Content-addressable memory,Associative property,Bidirectional associative memory,Computer science,Artificial intelligence,Artificial neural network,Hopfield network | Conference |
Volume | ISSN | Citations |
7095 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Juan Carbajal Hernández | 1 | 26 | 9.48 |
| Luis Sánchez | 2 | 36 | 13.87 |