Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

Mingo López, Fernando de; Muriel Fernández, Miguel Ángel; Gómez Blas, Nuria y Triviño G., Daniel (2012). Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.. "International Journal "Information Models and Analyses"", v. 1 (n. 3); pp. 203-219. ISSN 1314-6416.

Descripción

Título: Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.
Autor/es:
  • Mingo López, Fernando de
  • Muriel Fernández, Miguel Ángel
  • Gómez Blas, Nuria
  • Triviño G., Daniel
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal "Information Models and Analyses"
Fecha: 2012
Volumen: 1
Materias:
Palabras Clave Informales: Neural Networks, Swarm Computing, Particle Swarm Optimization.
Escuela: E.U. de Informática (UPM) [antigua denominación]
Departamento: Organización y Estructura de la Información [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 15817
Identificador DC: http://oa.upm.es/15817/
Identificador OAI: oai:oa.upm.es:15817
URL Oficial: http://www.foibg.com/ijima/vol01/ijima-cv01.htm
Depositado por: Memoria Investigacion
Depositado el: 07 Nov 2013 17:33
Ultima Modificación: 21 Abr 2016 16:07
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