Intuitionistic Fuzzy Radial Basis Functions Network for modeling of nonlinear dynamics

Yancho Todorov, Margarita Terziyska, Petia Koprinkova-Hristova

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

5 Citations (Scopus)


This paper describes a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. As a learning approach for the designed hybrid neural network, the gradient optimization procedure is proposed. To investigate the potentials of the generated structure throughout varying network parameters, the modeling of a twobenchmark chaotic time series – Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.
Original languageEnglish
Title of host publicationProceedings of the 2017 21st International Conference on Process Control
ISBN (Electronic)978-1-5386-4011-1
Publication statusPublished - 17 Jul 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Process Control - Štrbské Pleso, Slovakia
Duration: 6 Jun 20179 Jun 2017
Conference number: 21


ConferenceInternational Conference on Process Control
CityŠtrbské Pleso
Internet address


  • process control
  • modeling
  • artificial intelligence
  • fuzzy systems
  • neural networks
  • Fuzzy-neural networks


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