Particle filtering approach to membership function adjustment in fuzzy logic systems

Jun Ho Chung, Jung Min Pak, Choon Ki Ahn, Sung Hyun You, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

The fuzzy logic system has been a popular tool for modeling nonlinear systems in recent years. In the fuzzy logic system, the shape of the membership function has a significant effect on the modeling accuracy. Thus, membership function adjustment methods have been studied and developed. However, in highly nonlinear systems, the existing membership function adjustment method based on the extended Kalman filter (EKF) may exhibit poor performance due to the linearization error. In this paper, to overcome the drawback of the EKF-based membership function adjustment (EKFMFA), we propose a new membership function adjustment method based on the particle filter (PF). The proposed PF-based membership function adjustment (PFMFA) does not suffer from performance degradation due to the linearization error. We demonstrate that the PFMFA outperforms the EKFMFA through the simulation of a fuzzy cruise control system.

Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusAccepted/In press - 2015 Dec 28

Fingerprint

Fuzzy Logic
Membership functions
Fuzzy logic
Extended Kalman filters
Linearization
Nonlinear systems
Cruise control
Control systems
Degradation

Keywords

  • Fuzzy cruise control
  • Fuzzy logic system
  • Membership function
  • PF-based membership function adjustment

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Particle filtering approach to membership function adjustment in fuzzy logic systems. / Chung, Jun Ho; Pak, Jung Min; Ahn, Choon Ki; You, Sung Hyun; Lim, Myo Taeg; Song, Moon Kyou.

In: Neurocomputing, 28.12.2015.

Research output: Contribution to journalArticle

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