Neural Networks for Perception

Biological and Artificial Computation: From Neuroscience to Technology

Volume 1240 of the series Lecture Notes in Computer Science pp 1095-1106

Date:

A competitive neural network for blind separation of sources based on geometric properties

  • Alberto PrietoAffiliated withDepartamento de Electrónica y Tecnología de Computadores, Universidad de Granada
  • , Carlos G. PuntonetAffiliated withDepartamento de Electrónica y Tecnología de Computadores, Universidad de Granada
  • , Beatriz PrietoAffiliated withDepartamento de Electrónica y Tecnología de Computadores, Universidad de Granada
  • , Manuel Rodríguez-AlvarezAffiliated withDepartamento de Electrónica y Tecnología de Computadores, Universidad de Granada

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Abstract

This contribution presents a new approach to recover original signals (“sources”) from their linear mixtures, observed by the same number of sensors. The algorithm proposed assume that the input distributions are bounded and the sources generate certain combinations of boundary values. The method is simpler than other proposals and is based on geometric algebra properties. We present a neural network approach to show that with two networks, one for the separation of sources and one for weight learning, running in parallel, it is possible to efficiently recover the original signals. The learning rule is unsupervised and each computational element uses only local information.