Simple heuristic approach for training of type-2 NEO-fuzzy neural network

Yancho Todorov, Margarita Terziyska

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

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. As learning procedure a simple heuristic backpropagation approach, where the sign of the gradient is taken into account, is adopted. To improve the robustness of the network and the possibilities for handling uncertainties, Interval 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. A comparison is made with the classical Gradient Descent learning approach.

Original languageEnglish
Title of host publicationProceedings of the 2015 20th International Conference on Process Control, PC 2015
PublisherIEEE
Pages278-283
Number of pages6
Volume2015-July
ISBN (Electronic) 978-1-4673-6627-4
DOIs
Publication statusPublished - 28 Jul 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Process Control - Strbske Pleso, Slovakia
Duration: 9 Jun 201512 Jun 2015
Conference number: 20

Conference

ConferenceInternational Conference on Process Control
Abbreviated titlePC
CountrySlovakia
CityStrbske Pleso
Period09/06/201512/06/2015

Keywords

  • Chaotic time-series prediction
  • Dynamic modeling
  • Fuzzy systems
  • Neo-fuzzy neuron
  • Neural networks

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