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

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

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. Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction. Presented at 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)

• 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/
• Contract Date: Jun 10, 2016