| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 409362 | Neurocomputing | 2007 | 8 Pages |
Abstract
An efficient hysteretic Hopfield network with dynamic tunneling is proposed. The hysteretic activation function is used for training. The dynamic tunneling approach is employed to detrap the network from local minima. The network gives better convergence results for the selected problems namely crossbar switch problem with exclusive switching and concurrent switching, and NN-queens problem.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
P. Thangavel, D. Gladis,