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A fuzzy-neural model for co-ordination in air traffic flow management

  • Leïla Zerrouki
  • Bernadette Bouchon-Meunier
  • Rémy Fondacci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1566)

Abstract

The paper presents a methodological approach in the area of complex system studies. It provides a description of a model aimed at protecting air traffic sectors against overload in a large-scale air traffic system. In such a problem, different aspects must be taken into account: data uncertainty, complexity due to the large dimension of the air traffic system, structural and functional interactions, etc. The model proposed is a decentralised and co-ordinated system composed of a co-ordination level and a control level. The study points on the co-ordination level which decomposes the large sector network into several smaller overlapping subnetworks that can be controlled independently. A modified interaction prediction method is developed using a fuzzy model. This model provides the co-ordination parameters on the basis of imprecise data and an approximate reasoning. A specific inference mechanism based on a neural network is adopted in order to reduce time inference costs and provide a satisfying output.

Keywords

Large-scale System Decomposition Fuzzy Model Approximate Reasoning Neural Network Air Traffic Flow Management 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Leïla Zerrouki
    • 1
    • 2
  • Bernadette Bouchon-Meunier
    • 2
  • Rémy Fondacci
    • 1
  1. 1.INRETSArcueil
  2. 2.LIP6Paris Cedex 05

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