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. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combination of extreme learning machine (ELM) and artificial bee colony optimization (ABC) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. The effective forecasting performance of the proposed hybrid learning algorithm is analyzed by modeling a chaotic data set. It is found that the forecasted errors gradually decrease with decrease in the level of noise in data and vise versa.
Original language | English |
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Title of host publication | 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings |
Number of pages | 6 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 14 Dec 2016 |
Pages | 334-339 |
Article number | 7783237 |
ISBN (Electronic) | 9781509051342 |
DOIs | |
Publication status | Published - 14 Dec 2016 |
Externally published | Yes |
Event | 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Kuala Lumpur, Malaysia Duration: 15 Aug 2016 → 17 Aug 2016 |
Conference
Conference | 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 |
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Country | Malaysia |
City | Kuala Lumpur |
Period | 15/08/2016 → 17/08/2016 |
Keywords
- Artificial bee colony optimization
- Extreme learning machine
- Forecasting
- Hybrid learning algorithm
- Interval type-2 fuzzy logic system