Recurrent fuzzy-neural network with fast learning algorithm for predictive control

Yancho Todorov, Margarita Terzyiska, Michail Petrov

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783642407277
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Neural Networks - Sofia, Bulgaria
Duration: 10 Sep 201313 Sep 2013
Conference number: 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Artificial Neural Networks
Abbreviated titleICANN


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


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