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 |
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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 | |
Publication status | Published - 6 Feb 2017 |
Externally published | Yes |
Event | 2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Hungary Duration: 9 Oct 2016 → 12 Oct 2016 http://smc2016.org/ https://ieeexplore.ieee.org/xpl/conhome/7830913/proceeding |
Conference
Conference | 2016 IEEE International Conference on Systems, Man, and Cybernetics |
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Country/Territory | Hungary |
City | Budapest |
Period | 09/10/2016 → 12/10/2016 |
Internet address |
Keywords
- Extreme learning machine
- Hybrid learning algorithm
- Interval type-2 fuzzy logic system
- NSGAII