Observatorio de I+D+i UPM

Descripción
Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.
Internacional	Si
JCR del ISI	No
Título de la revista	Journal of Computational Science
ISSN	1877-7503
Factor de impacto JCR	0,14
Información de impacto	Scopus: h-index = 3 / SJR 2011 = 0.146 / Category = Theoretical Computer Science -- Q1 (5/78)
Volumen	1
DOI	10.1016/j.jocs.2011.12.005
Número de revista	2
Desde la página	46
Hasta la página	55
Mes	SIN MES
Ranking	Q1 en scopus

Esta actividad pertenece a memorias de investigación

• Autor: Fernando de Mingo Lopez (UPM)
• Autor: Nuria Gomez Blas (UPM)
• Autor: Alberto Arteta Albert (UPM)

• Creador: Grupo de Investigación: Grupo de Señal Fotónica
• Grupo de Investigación: Grupo de Computación Natural
• Departamento: Matemática Aplicada (E.U. Informática)