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Memorias de investigación
Artículos en revistas:
Multilayered neural architectures evolution for computing sequences of orthogonal polynomials
Año:2018
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
This article presents an evolutionary algorithm to autonomously construct full-connected multilayered feedforward neural architectures. This algorithm employs grammar-guided genetic programming with a context-free grammar that has been specifically designed to satisfy three important restrictions. First, the sentences that belong to the language produced bythe grammar only encode all valid neural architectures. Second, full-connected feedforwardneural architectures of any size can be generated. Third, smaller-sized neural architecturesare favored to avoid overfitting. The proposed evolutionary neural architectures construction system is applied to compute the terms of the two sequences that define the three-term recurrence relation associated with a sequence of orthogonal polynomials. This application imposes an important constraint: training datasets are always very small. Therefore, an adequate sized neural architecture has to be evolved to achieve satisfactory results, which arepresented in terms of accuracy and size of the evolved neural architectures, and convergencespeed of the evolutionary process.
Internacional
Si
JCR del ISI
Si
Título de la revista
Annals of Mathematics And Artificial Intelligence
ISSN
1012-2443
Factor de impacto JCR
0,807
Información de impacto
Datos JCR del año 2016
Volumen
84
DOI
10.1007/s10472-018-9601-2
Número de revista
3-4
Desde la página
161
Hasta la página
184
Mes
DICIEMBRE
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Participantes
  • Autor: Maria Dolores Barrios Rolania (UPM)
  • Autor: Guillermo Delgado Martínez (UPM)
  • Autor: Daniel Manrique Gamo (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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