Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
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 combin...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2016
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010426205&doi=10.1109%2fICCOINS.2016.7783237&partnerID=40&md5=4b6f671147a8d732e6a7748feb7a7432 http://eprints.utp.edu.my/30486/ |
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| Summary: | 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. © 2016 IEEE. |
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