Fuzzy inference systems provide a simple yet powerful solution to complex non-linear problems, which have been widely and successfully applied in the control field. The TSK-based fuzzy inference approaches, such as the convention TSK, interval type 2 (IT2) TSK and their extensions TSK+ and IT2 TSK+ approaches, are more convenient to be employed in the control field, as they directly produce crisp outputs. This paper systematically reviews those four TSK-based inference approaches, and evaluates them empirically by applying them to a well-known cart centering control problem. The experimental results confirm the power of TSK+ and IT2 TSK+ approaches in enhancing the inference using either dense or sparse rule bases. © 2019, Springer Nature Switzerland AG.
|Title of host publication||Lecture Notes in Computer Science|
|Subtitle of host publication||including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics|
|Publication status||Published - 3 Aug 2019|
|Event||12th International Conference on Intelligent Robotics and Applications - Shenyang, China|
Duration: 8 Aug 2019 → 11 Aug 2019
|Name||Intelligent Robotics and Applications|
|Conference||12th International Conference on Intelligent Robotics and Applications|
|Abbreviated title||ICIRA 2019|
|Period||8/08/19 → 11/08/19|
Li, J., Chao, F., & Yang, L. (2019). A Study of TSK Inference Approaches for Control Problems. In Lecture Notes in Computer Science : including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (Vol. 11743 LNAI, pp. 195-207). [Chapter 17] (Intelligent Robotics and Applications; Vol. 11743). Springer.