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Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction

Eyoh, Imo; John, Robert; de Maere, Geert

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Authors

Imo Eyoh

Robert John



Abstract

This paper presents an approach to prediction based on a new interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference. The gradient descent algorithm (GDA) is used to adapt the parame- ters of the IT2IFLS. The empirical comparison is made on the designed system using two synthetic datasets. Analysis of our results reveal that the presence of additional degrees of freedom in terms of non-membership functions and hesitation indexes in IT2IFLS tend to reduce the root mean square error (RMSE) of the system compared to a type-1 fuzzy logic approach and some interval type-2 fuzzy systems.

Citation

Eyoh, I., John, R., & de Maere, G. (2016). Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction.

Conference Name 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
End Date Oct 12, 2016
Acceptance Date Jun 1, 2016
Publication Date Oct 11, 2016
Deposit Date Jun 10, 2016
Publicly Available Date Oct 11, 2016
Peer Reviewed Peer Reviewed
Public URL https://nottingham-repository.worktribe.com/output/823969
Related Public URLs http://www.smc2016.org/

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