A Study of TSK Inference Approaches for Control Problems

Jie Li, Fei Chao, Longzhi Yang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
EditorsHaibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
PublisherSpringer
Pages195-207
Number of pages13
ISBN (Print)9783030275372
DOIs
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11743 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
CountryChina
CityShenyang
Period8/08/1911/08/19

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