Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Artificial neural networks in wood identification: the case of two Juniperus species from the canary islands.
Year:2009
Research Areas
  • Organic materials
Information
Abstract
Neural networks are complex mathematical structures inspired on biological neural networks, capable of learning from examples (training group) and extrapolating knowledge to an unknown sample (testing group). The similarity of wood structure in many species, particularly in the case of conifers, means that they cannot be differentiated using traditional methods. The use of neural networks can be an effective tool for identifying similar species with a high percentage of accuracy. This predictive method was used to differentiate Juniperus cedrus and J. phoenicea var. canariensis, both from the Canary Islands. The anatomical features of their wood are so similar that it is not possible to differentiate them using traditional methods. An artificial neural network was used to determine if this method could differentiate the two species with a high degree of probability through the biometry of their anatomy. To achieve the differentiation, a feedforward multilayer percepton network was designed, which attained 98.6% success in the training group and 92.0% success in the testing or unknown group. The proposed neural network is satisfactory for the desired purpose and enables J. cedrus and J. phoenicea var. canariensis to be differentiated with a 92% probability.
International
Si
JCR
Si
Title
IAWA JOURNAL
ISBN
0928-1541
Impact factor JCR
1
Impact info
Volume
30
Journal number
1
From page
87
To page
94
Month
ENERO
Ranking
Participants
  • Autor (4): RUTH MORENO ROMERO (UPM)
  • Autor: Luis Garcia Esteban (UPM)
  • Autor: Francisco Garcia Fernandez (UPM)
  • Autor: Paloma de Palacios De Palacios (UPM)
  • Autor: Nieves Navarro Cano (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Tecnología de la Madera y el Corcho
  • Departamento: Ingeniería Forestal
  • Departamento: Construcciones Arquitectónicas y su Control
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
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