TY - CHAP
T1 - A Study of TSK Inference Approaches for Control Problems
AU - Li, Jie
AU - Chao, Fei
AU - Yang, Longzhi
PY - 2019/8/3
Y1 - 2019/8/3
N2 - 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.
AB - 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.
M3 - Chapter
SN - 0302-9743
VL - 11743 LNAI
T3 - Intelligent Robotics and Applications
SP - 195
EP - 207
BT - Lecture Notes in Computer Science
PB - Springer
T2 - 12th International Conference on Intelligent Robotics and Applications
Y2 - 8 August 2019 through 11 August 2019
ER -