| Autor: |
Carbonell, Jaime G., Siekmann, Jörg, Hertzberg, Joachim, Beetz, Michael, Englert, Roman, López-Rodríguez, Domingo, Mérida-Casermeiro, Enrique, Galán-Marín, Gloria, Ortiz-de-Lazcano-Lobato, Juan M. |
| Zdroj: |
KI 2007: Advances in Artificial Intelligence; 2007, p397-411, 15p |
| Abstrakt: |
In this work, a new stochastic method for optimization problems is developed. Its theoretical bases guaranteeing the convergence of the method to a minimum of the objective function are presented, by using quite general hypotheses. Its application to recurrent discrete neural networks is also developed, focusing in the multivalued MREM model, a generalization of Hopfield's. In order to test the efficiency of this new method, we study the well-known Traveling Salesman Problem. Experimental results will show that this new model outperforms other techniques, achieving better results, even on average, than other methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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