Un modelo de redes neuronales para complementariedad no lineal

Favián Arenas, Rosana Pérez, Hevert Vivas

Resumen


En este artículo presentamos un modelo de red neuronal para resolver el problema de complementariedad no lineal. Para ello, reformulamos este problema como uno de minimización sin restricciones usando una familia uniparamétrica de funciones de complementariedad. Demostramos resultados de existencia y convergencia de la trayectoria de la red neuronal, así como resultados de estabilidad en el sentido de Lyapunov, estabilidad asintótica y exponencial. Además, presentamos resultados numéricos preliminares que ilustran un buen desempeño práctico del modelo.

Palabras clave: Red neuronal, problema de complementariedad no lineal, estabilidad, reformulación

Para citar este artículo: F. Arenas, R. Pérez, H. Vivas, Un modelo de redes neuronales para complementariedad no lineal, Rev. Integr. Temas Mat. 34 (2016), No. 2, 169-185


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DOI: http://dx.doi.org/10.18273/revint.v34n2-2016005

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