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

Fingerprint

Chaotic Time Series
Fuzzy neural networks
Fuzzy Neural Network
Fuzzy sets
Time series
Topology
Glass
Interval
Modeling
Multiple Zeros
Complex Dynamics
Network Topology
Fuzzy Sets
Gradient
Robustness
Uncertainty
Approximation

Keywords

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

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
Todorov, Yancho ; Terziyska, Margarita. / Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network. Artificial Neural Networks and Machine Learning, ICANN 2014: 24th International Conference on Artificial Neural Networks. Springer, 2014. pp. 643-650 (Lecture Notes in Computer Science, Vol. 8681).
@inproceedings{a1d90db44c994a40b1fa7061fbac5434,
title = "Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network",
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.",
keywords = "chaotic time-series prediction, dynamic modeling, neo-fuzzy neuron, neural networks, type-2 fuzzy set",
author = "Yancho Todorov and Margarita Terziyska",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-11179-7_81",
language = "English",
isbn = "978-3-319-11178-0",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "643--650",
booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2014",
address = "Germany",

}

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. Springer, Lecture Notes in Computer Science, vol. 8681, pp. 643-650, 24th International Conference on Artificial Neural Networks, ICANN 2014, Hamburg, Germany, 15/09/14. https://doi.org/10.1007/978-3-319-11179-7_81

Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network. / Todorov, Yancho; Terziyska, Margarita.

Artificial Neural Networks and Machine Learning, ICANN 2014: 24th International Conference on Artificial Neural Networks. Springer, 2014. p. 643-650 (Lecture Notes in Computer Science, Vol. 8681).

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

TY - GEN

T1 - Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network

AU - Todorov, Yancho

AU - Terziyska, Margarita

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - chaotic time-series prediction

KW - dynamic modeling

KW - neo-fuzzy neuron

KW - neural networks

KW - type-2 fuzzy set

UR - http://www.scopus.com/inward/record.url?scp=84958542980&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-11179-7_81

DO - 10.1007/978-3-319-11179-7_81

M3 - Conference article in proceedings

AN - SCOPUS:84958542980

SN - 978-3-319-11178-0

T3 - Lecture Notes in Computer Science

SP - 643

EP - 650

BT - Artificial Neural Networks and Machine Learning, ICANN 2014

PB - Springer

ER -

Todorov Y, Terziyska M. 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. Springer. 2014. p. 643-650. (Lecture Notes in Computer Science, Vol. 8681). https://doi.org/10.1007/978-3-319-11179-7_81