Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network

Yancho Todorov, Margarita Terziyska

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

7 Citations (Scopus)

Abstract

This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. The induced gradient based learning procedure, adjusts solely the consequent network parameters. To improve the robustness of the network and the possibilities for handling uncertainties, Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014
Subtitle of host publication24th International Conference on Artificial Neural Networks
PublisherSpringer
Pages643-650
ISBN (Electronic)978-3-319-11179-7
ISBN (Print)978-3-319-11178-0
DOIs
Publication statusPublished - 1 Jan 2014
MoE publication typeA4 Article in a conference publication
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: 15 Sep 201419 Sep 2014

Publication series

SeriesLecture Notes in Computer Science
Volume8681
ISSN0302-9743

Conference

Conference24th International Conference on Artificial Neural Networks, ICANN 2014
CountryGermany
CityHamburg
Period15/09/1419/09/14

Keywords

  • chaotic time-series prediction
  • dynamic modeling
  • neo-fuzzy neuron
  • neural networks
  • type-2 fuzzy set

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  • Cite this

    Todorov, Y., & Terziyska, M. (2014). Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network. In Artificial Neural Networks and Machine Learning, ICANN 2014: 24th International Conference on Artificial Neural Networks (pp. 643-650). Springer. Lecture Notes in Computer Science, Vol.. 8681 https://doi.org/10.1007/978-3-319-11179-7_81