Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics

Yancho Todorov, Petia Koprinkova-Hristova, Margarita Terziyska

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

2 Citations (Scopus)

Abstract

This paper deals with 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 of 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. A simplified gradient optimization procedure as a learning approach for the designed hybrid neural network is proposed. To investigate the effects of the generated structure throughout varying network parameters, the modeling of a two benchmark 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, PC 2017
EditorsM. Kvasnica, M. Fikar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages410-415
Number of pages6
ISBN (Electronic)978-1-5386-4011-1
DOIs
Publication statusPublished - 11 Jul 2017
MoE publication typeA4 Article in a conference publication
Event21st International Conference on Process Control, PC 2017 - Strbske Pleso, Slovakia
Duration: 6 Jun 20179 Jun 2017

Conference

Conference21st International Conference on Process Control, PC 2017
CountrySlovakia
CityStrbske Pleso
Period6/06/179/06/17

Keywords

  • chaotic time series
  • intuitionistic fuzzy logic
  • neural networks
  • Radial Basis Functions Network
  • uncertanties

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  • Cite this

    Todorov, Y., Koprinkova-Hristova, P., & Terziyska, M. (2017). Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics. In M. Kvasnica, & M. Fikar (Eds.), Proceedings of the 2017 21st International Conference on Process Control, PC 2017 (pp. 410-415). [7976249] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PC.2017.7976249