A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems

Saima Hassan, Mojtaba A. Khanesar, Jafreezal Jaafar, Abbas Khosravi

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The major challenge in the design of Interval type-2 fuzzy logic system (IT2FLS) is to determine the optimal parameters for their antecedent and consequent parts. The most frequently used objective function for the design of IT2FLSs is root mean squared error (RMSE). However, other than RMSE, the maximum absolute error (MAE) for each of identification samples is very important. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed algorithm benefits from the combination of extreme learning machine (ELM) and non-dominated sorting genetic algorithm (NSGAII) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. The proposed method is used for forecasting of nonlinear dynamic systems. It is shown that not only the proposed method results in low RMSE, MAE achieved is also satisfactory.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date6 Feb 2017
Pages155-160
Article number7844235
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 6 Feb 2017
Externally publishedYes
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period09/10/201612/10/2016

Keywords

  • Extreme learning machine
  • Hybrid learning algorithm
  • Interval type-2 fuzzy logic system
  • NSGAII

Cite this

Hassan, S., Khanesar, M. A., Jaafar, J., & Khosravi, A. (2017). A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 155-160). [7844235] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844235