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

Hassan, S. and Khanesar, M.A. and Jaafar, J. and Khosravi, A. (2017) 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. 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. © 2016 IEEE.

Item Type: Article
Impact Factor: cited By 0
Departments / MOR / COE: Division > Academic > Faculty of Science & Information Technology > Computer Information Sciences
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 22 Apr 2018 14:43
Last Modified: 22 Apr 2018 14:43
URI: http://scholars.utp.edu.my/id/eprint/20157

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