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Asymmetric Continuous-Time Neural Networks without Local Traps for Solving Constraint Satisfaction Problems
There has been a long history of using neural networks for combinatorial optimization and constraint satisfaction problems. Symmetric Hopfield networks and similar approaches use steepest descent dynamics, and they always converge to the closest local minimum of the energy landscape. For finding glo...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2013
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3774769/ https://www.ncbi.nlm.nih.gov/pubmed/24066045 http://dx.doi.org/10.1371/journal.pone.0073400 |