Fuzzy granular neural network for incremental modeling of nonlinear chaotic systems

Daniel Leite, Marcio Santana, Ana Borges, Fernando Gomide

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

7 Scopus citations

Abstract

Evolving intelligent systems are useful for processing online data streams. This paper presents an evolving granular neuro-fuzzy modeling framework and an application example on the modeling of the Rossler chaos. The evolving Granular Neural Network (eGNN) is able to deal with new events of nonstationary environments using fuzzy information granules and different types of aggregation neurons. An incremental learning algorithm builds the network topology from spatio-Temporal features of a data stream. The goal is to obtain more abstract representations of large amounts of data, and thereafter provide accurate one-step predictions and insights about the phenomenon that generates the data. Results suggest that eGNN learns successfully from a data stream generated by the Rossler nonlinear equations. Additionally, eGNN has shown to be competitive with state-of-The-Art data-driven modeling approaches.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-71
Number of pages8
ISBN (Electronic)9781509006250
DOIs
StatePublished - 7 Nov 2016
Externally publishedYes
Event2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

Conference

Conference2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

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