Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling

BAĞIŞ A., KONAR M.

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, vol.38, no.5, pp.579-592, 2016 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 5
  • Publication Date: 2016
  • Doi Number: 10.1177/0142331215591239
  • Journal Name: TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.579-592
  • Keywords: Artificial bee colony, Sugeno model, Mamdani model, nonlinear system modelling, RECTANGULAR MICROSTRIP ANTENNAS, RULE BASE, STRUCTURE IDENTIFICATION, GENETIC ALGORITHM, TABU SEARCH, BANDWIDTH, DESIGN, LOGIC, THIN
  • Erciyes University Affiliated: Yes

Abstract

This paper is an extended version of the paper presented at TOK 2014 (Turkish Automatic Control National Meeting) which examined the determination of Sugeno type fuzzy model parameters optimized by the artificial bee colony (ABC) algorithm for a microstrip antenna. This paper presents a performance comparison of the Sugeno and Mamdani type fuzzy models proposed for nonlinear system modelling. To determine the parameters of the fuzzy models, the ABC algorithm is used. For this purpose, several nonlinear system examples which given in the literature were considered, and the results obtained by the optimized fuzzy models were compared with the other modelling approaches in the literature. Simulation results demonstrate that the use of the ABC algorithm provides a remarkable contribution to the model's performance.