Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.
Year:2012
Research Areas
  • Artificial intelligence
Information
Abstract
This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.
International
Si
JCR
No
Title
International Journal on Information Technologies and Knowledge
ISBN
1313-0455
Impact factor JCR
Impact info
Volume
5
Journal number
2
From page
1
To page
17
Month
SIN MES
Ranking
Participants
  • Autor: Fernando de Mingo Lopez (UPM)
  • Autor: Miguel Angel Muriel Fernandez (UPM)
  • Autor: Nuria Gomez Blas (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Señal Fotónica
  • Grupo de Investigación: Grupo de Computación Natural
  • Departamento: Tecnología Fotónica y Bioingeniería
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Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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