Name
Abstract | ||
|---|---|---|
A probabilistic version of the binary Hopfield networks is proposed. Operation of the network is completely parallel, in the sense that evolution of each unit is governed only by its inherent probabilistic law. It is shown that the global state is attracted by one of the equilibria with probability one |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1109/21.105090 | Systems, Man and Cybernetics, IEEE Transactions |
Keywords | Field | DocType |
neural nets,parallel processing,probability,binary Hopfield networks,global state,neural networks,probabilistic model,probability,static attractors | Computer science,Recurrent neural network,Probabilistic neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Probabilistic logic,Deep learning,Cellular neural network,Hopfield network,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 4 | 0018-9472 |
Citations | PageRank | References |
1 | 0.70 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuo Yamanaka | 1 | 16 | 5.69 |
| Masahiro Agu | 2 | 4 | 2.92 |