Dynamical neural schema for quadratic discrete optimizations problems

E. Goles; G. Hernandez; M. Matamala

doi:10.1016/0893-6080(88)90135-9

Dynamical neural schema for quadratic discrete optimizations problems

E. Goles, G. Hernandez, M. Matamala

Producción científica: Contribución a una revista › Artículo de la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}ⁿ, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).

Idioma original	Inglés
Páginas (desde-hasta)	96
Número de páginas	1
Publicación	Neural Networks
Volumen	1
N.º	1 SUPPL
DOI	https://doi.org/10.1016/0893-6080(88)90135-9
Estado	Publicada - 1988
Publicado de forma externa	Sí
Evento	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duración: 6 sep. 1988 → 10 sep. 1988

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title = "Dynamical neural schema for quadratic discrete optimizations problems",

abstract = "We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}n, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).",

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AU - Hernandez, G.

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AB - We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}n, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).

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Dynamical neural schema for quadratic discrete optimizations problems

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