Evolving Systems

, Volume 2, Issue 1, pp 25–33 | Cite as

Improving premise structure in evolving Takagi–Sugeno neuro-fuzzy classifiers

  • Abdullah AlmaksourEmail author
  • Eric Anquetil
Original Paper


We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi–Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance.


Incremental learning Takagi–Sugeno Neuro-fuzzy 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.INSA de Rennes/UMR IRISARennesFrance

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