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An Hybrid System for Continuous Learning

  • Aldo Franco Dragoni
  • Germano Vallesi
  • Paola Baldassarri
  • Mauro Mazzieri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

We propose a Multiple Neural Networks system for dynamic environments, where one or more neural nets could no longer be able to properly operate, due to partial changes in some of the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net’s degree of reliability is defined as the probability that the net is giving the desired output, in case of conflicts between the outputs of the various nets the re-evaluation of their degrees of reliability can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying two algorithms, the Inclusion based and the Weighted one over all the maximally consistent subsets of the global outcome.

Keywords

Multiple Neural Networks Hybrid System Bayesian Conditioning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Aldo Franco Dragoni
    • 1
  • Germano Vallesi
    • 1
  • Paola Baldassarri
    • 1
  • Mauro Mazzieri
    • 1
  1. 1.Department of Ingegneria Informatica, Gestionale e dell’Automazione (DIIGA)Università Politecnica delle MarcheAnconaItaly

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