Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Learning in Networks of Evolutionary Processors-
Year:2010
Research Areas
  • Electronic technology and of the communications
Information
Abstract
This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neu- ral Networks that are able to approximate any function or classify any pattern set provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised learning stage in order to perform desired response. Concerning symbolic information new research area has been developed, inspired by George Paun, called Membrane Sys- tems. A step forward, in a similar Neural Network architecture, was done to obtain Networks of Evolutionary Processors (NEP). A NEP is a set of processors connected by a graph, each processor only deals with sym- bolic information using rules. In short, objects in processors can evolve and pass through processors until a stable con guration is reach. This paper shows some ideas about these two models and how to incorpo- rate a learning stage, based on self-organizing algorithms, in networks of evolutionary processors.
International
Si
JCR
No
Title
IJDEM International Journal on Data Engineering and Management
ISBN
1947-8534
Impact factor JCR
0
Impact info
Volume
Journal number
From page
34
To page
41
Month
ENERO
Ranking
Participants
  • Autor: Fernando de Mingo Lopez (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
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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)