Interval Type-2 TSK+ Fuzzy Inference System

Jie Li, Longzhi Yang, Xin Fu, Fei Chao, Yanpeng Qu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)
22 Downloads (Pure)


Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN (Electronic)978-1-5090-6020-7
ISBN (Print)978-1-5090-6021-4
Publication statusPublished - 15 Oct 2018
EventIEEE World Congress on Computational Intelligence 2018 - Windsor Barra Convention Centre, Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018


ConferenceIEEE World Congress on Computational Intelligence 2018
Abbreviated titleWCCI 2018
CityRio de Janeiro


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