Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data

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

doi:10.1109/ICCOINS.2016.7783237

Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data

Saima Hassan, Jafreezal Jaafar, Mojtaba A. Khanesar, 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. 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
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	https://doi.org/10.1109/ICCOINS.2016.7783237
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
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

Access to Document

10.1109/ICCOINS.2016.7783237

Link to publication in Scopus

Cite this

Hassan, S., Jaafar, J., Khanesar, M. A., & Khosravi, A. (2016). Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data. In 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings (pp. 334-339). [7783237] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCOINS.2016.7783237

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Hassan, S, Jaafar, J, Khanesar, MA & Khosravi, A 2016, Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data. in 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings., 7783237, Institute of Electrical and Electronics Engineers Inc., pp. 334-339, 3rd International Conference on Computer and Information Sciences, ICCOINS 2016, Kuala Lumpur, Malaysia, 15/08/2016. https://doi.org/10.1109/ICCOINS.2016.7783237

Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data. / Hassan, Saima; Jaafar, Jafreezal; Khanesar, Mojtaba A.; Khosravi, Abbas.

2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 334-339 7783237.

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

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Hassan S, Jaafar J, Khanesar MA, Khosravi A. Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data. In 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 334-339. 7783237 https://doi.org/10.1109/ICCOINS.2016.7783237