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A Study of TSK Inference Approaches for Control Problems

  • Jie Li
  • Fei Chao
  • Longzhi YangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)

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.

Keywords

TSK TSK+ Fuzzy control Fuzzy inference Sparse rule base 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computing and Digital TechnologiesTeesside UniversityMiddlesbroughUK
  2. 2.Department of Cognitive ScienceXiamen UniversityXiamenChina
  3. 3.Department of Computer and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

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