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 contributionScientificpeer-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 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Pages643-650
Number of pages8
ISBN (Print)9783319111780
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Neural Networks - Hamburg, Germany
Duration: 15 Sep 201419 Sep 2014
Conference number: 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Neural Networks
Abbreviated titleICANN
CountryGermany
CityHamburg
Period15/09/201419/09/2014

Keywords

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

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