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Automatic Tuning of Fuzzy Partitions in Inductive Reasoning

  • Angela Nebot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

The aim of this research is to automatically tuning a good fuzzy partition, i.e. determine the number of classes of each system variable, in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR is an inductive methodology for modelling and simulate those systems from which no previous structural knowledge is available. The first step of FIR methodology is the fuzzification process that converts quantitative variables into fuzzy qualitative variables. In this process it is necessary to define the number of classes into which each variable is going to be discretized. In this paper an algorithm based on simulated annealing is developed to suggest a good partition in an automatic way. The proposed algorithm is applied to an environmental system.

Keywords

Cost Function Ozone Concentration Candidate Solution Simulated Annealing Algorithm Fuzzy Partition 
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 2003

Authors and Affiliations

  • Angela Nebot
    • 1
  1. 1.Departament de Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

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