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A Self-Organized Fuzzy Neural Network Approach for Rule Generation of Fuzzy Logic Systems

  • Juan C. Figueroa-García
  • Cynthia Ochoa-Rey
  • Jose Avellaneda-González
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

This paper shows an algorithm for creating fuzzy logic systems from data by synchronizing its fuzzy sets and rules using a novel neuro fuzzy approach to generate rules and fuzzy sets from analyzing input data. A volatile time series example is solved and analyzed using the residuals of the model.

Keywords

Fuzzy Logic System Gaussian Membership Function Neuro Fuzzy Approach Neural Fuzzy System Linear Stochastic Process 
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 2013

Authors and Affiliations

  • Juan C. Figueroa-García
    • 1
  • Cynthia Ochoa-Rey
    • 1
  • Jose Avellaneda-González
    • 1
  1. 1.Universidad Distrital Francisco José de CaldasBogotáColombia

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