Exploiting the Functional Training Approach in Takagi-Sugeno Neuro-fuzzy Systems

  • Cristiano L. Cabrita
  • António E. Ruano
  • Pedro M. Ferreira
  • László T. Kóczy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)


When used for function approximation purposes, neural networks and neuro-fuzzy systems belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of parameter separability can also be applied when the training problem is formulated as the minimization of the integral of the (functional) squared error, over the input domain. Using this approach, the computation of the derivatives involves terms that are dependent only on the model and the input domain, and terms which are the projection of the target function on the basis functions and on their derivatives with respect to the nonlinear parameters, over the input domain. These later terms can be numerically computed with the data.

The use of the functional approach is introduced here for Takagi-Sugeno models. An example shows that this approach obtains better results than the standard, discrete technique, as the performance surface employed is more similar to the one obtained with the function underlying the data. In some cases, as shown in the example, a complete analytical solution can be found.


Takagi-Sugeno models Functional training Parameter separability 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristiano L. Cabrita
    • 1
  • António E. Ruano
    • 2
  • Pedro M. Ferreira
    • 3
  • László T. Kóczy
    • 4
  1. 1.University of AlgarveFaroPortugal
  2. 2.Centre for Intelligent SystemsIDMEC, IST and the University of AlgarveFaroPortugal
  3. 3.Algarve STP – Algarve Science & Technology ParkUniversity of AlgarveFaroPortugal
  4. 4.Faculty of Engineering SciencesSzéchenyi István UniversityGyõrHungary

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