A Study of TSK Inference Approaches for Control Problems

Jie Li, Fei Chao, Longzhi Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

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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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
Subtitle of host publicationincluding subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
Volume11743 LNAI
ISBN (Print)0302-9743
Publication statusPublished - 3 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameIntelligent Robotics and Applications
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2019


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