Recurrent Fuzzy-Neural Network with Fast Learning Algorithm for Predictive Control

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)


This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series – Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.


recurrent fuzzy-neural networks Takagi-Sugeno predictive control optimization Gradient descent Gauss-Newton method momentum learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Technical University-SofiaPlovdivBulgaria

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