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A Genetic Approach to the Automatic Generation of Fuzzy Control Systems from Numerical Controllers

  • Giuseppe Della Penna
  • Francesca Fallucchi
  • Benedetto Intrigila
  • Daniele Magazzeni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4733)

Abstract

Control systems are small components that control the behavior of larger systems. In the last years, sophisticated controllers have been widely used in the hardware/software embedded systems contained in a growing number of everyday products and appliances. Therefore, the problem of the automatic synthesis of controllers is extremely important. To this aim, several techniques have been applied, like cell-to-cell mapping, dynamic programming and, more recently, model checking. The controllers generated using these techniques are typically numerical controllers that, however, often have a huge size and not enough robustness. In this paper we present an automatic iterative process, based on genetic algorithms, that can be used to compress the huge information contained in such numerical controllers into smaller and more robust fuzzy control systems.

Keywords

Genetic Algorithm Membership Function Model Check Fuzzy System Fuzzy Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Giuseppe Della Penna
    • 1
  • Francesca Fallucchi
    • 3
  • Benedetto Intrigila
    • 2
  • Daniele Magazzeni
    • 1
  1. 1.Department of Computer Science, University of L’AquilaItaly
  2. 2.Department of Mathematics, University of Roma “Tor Vergata”Italy
  3. 3.DISP, University of Roma “Tor Vergata”Italy

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