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Interval type-2 intuitionistic fuzzy logic system for time series and identification problems - a comparative study

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posted on 12.02.2020 by Imo Eyoh, Jerry Eyoh, Roy Kalawsky
This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Journal of Fuzzy Logic Systems

Volume

10

Issue

1

Pages

1 - 17

Publisher

Wireilla Scientific Publications

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access article. It is published by Wireilla Scientific Publications under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/.

Publication date

2020-01-31

Copyright date

2020

ISSN

1839-6283

Language

en

Depositor

Prof Roy Kalawsky. Deposit date: 11 February 2020

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