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Nonlinear System Identification with Neurofuzzy Methods

  • Oliver Nelles

Abstract

This chapter discusses nonlinear system identification with neurofuzzy methods. In a general part, summary and overview of the most important types of fuzzy models are given. Their properties, advantages, and drawbacks are illustrated. In a more specific part a new algorithm for the construction of Takagi-Sugeno fuzzy systems is presented in detail. It is successfully applied to the identification of two nonlinear dynamic real-world processes.

Keywords

Membership Function Fuzzy System Fuzzy Model Validity Function Gaussian Membership Function 
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 Science+Business Media New York 1997

Authors and Affiliations

  • Oliver Nelles
    • 1
  1. 1.Institut für RegelungstechnikTechnische Hochschule DarmstadtDarmstadtGermany

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