Observatorio de I+D+i UPM

Memorias de investigación
Conferences:
Bio-Inspired Learning: Artificial Metaplasticty
Year:2011
Research Areas
  • Engineering
Information
Abstract
Metaplasticity concept was defined in 1996 by W.C. Abraham and presently is a biological concept widely known in the fields of biology and medicine: neuroscience, physiology, neurology and others. Inspired in it, outstanding improvements have been achieved in artificial neural networks design applied to pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon?s information theory. The concept is applicable to Artificial Neural Networks in general, although in this presentation it is centered on Multilayer Perceptrons (MLP). During the training phase, the Artificial Metaplasticity Multilayer Perceptron (AMMLP) algorithm gives higher values for updating the weights in the less frequent activations than in the more frequent ones. AMMLP achieves a more efficient training, while improving MLP performance. Tested in standard, well known and easy available Databases, its results are superior to the rest of algorithms, no matter what multidisciplinary application used as case study
International
Si
978-1-4577-1122-0
Entity
IEEE SMC
Entity Nationality
E.E.U.U. DE AMERICA
Place
Salamanca, España
Participants
  • Autor: Diego Andina De la Fuente (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
  • Departamento: Señales, Sistemas y Radiocomunicaciones
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)