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Interval type-2 intuitionistic fuzzy logic system for time series and identification problems - a comparative study
journal contribution
posted on 2020-02-12, 13:27 authored by Imo Eyoh, Jerry EyohJerry Eyoh, Roy KalawskyRoy KalawskyThis 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 SystemsVolume
10Issue
1Pages
1 - 17Publisher
Wireilla Scientific PublicationsVersion
- VoR (Version of Record)
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© The AuthorsPublisher 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
2020-01-31Copyright date
2020ISSN
1839-6283Publisher version
Language
- en
Depositor
Prof Roy Kalawsky. Deposit date: 11 February 2020Usage metrics
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