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

doi:10.1109/SMC.2016.7844235

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 proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Number of pages	6
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication date	6 Feb 2017
Pages	155-160
Article number	7844235
ISBN (Electronic)	9781509018970
DOIs	https://doi.org/10.1109/SMC.2016.7844235
Publication status	Published - 6 Feb 2017
Externally published	Yes
Event	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 9 Oct 2016 → 12 Oct 2016

Conference

Conference	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Country	Hungary
City	Budapest
Period	09/10/2016 → 12/10/2016

Keywords

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

Access to Document

10.1109/SMC.2016.7844235

Link to publication in Scopus

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

Hassan, Saima ; Khanesar, Mojtaba A. ; Jaafar, Jafreezal ; Khosravi, Abbas. / A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 155-160

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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.",

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Hassan, S, Khanesar, MA, 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., 7844235, Institute of Electrical and Electronics Engineers Inc., pp. 155-160, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 09/10/2016. https://doi.org/10.1109/SMC.2016.7844235

A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems. / Hassan, Saima; Khanesar, Mojtaba A.; Jaafar, Jafreezal; Khosravi, Abbas.

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 155-160 7844235.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Hassan S, Khanesar MA, Jaafar J, Khosravi A. 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. Institute of Electrical and Electronics Engineers Inc. 2017. p. 155-160. 7844235 https://doi.org/10.1109/SMC.2016.7844235