TY  - CONF
ID  - scholars6482
N2  - 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.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010426205&doi=10.1109%2fICCOINS.2016.7783237&partnerID=40&md5=4b6f671147a8d732e6a7748feb7a7432
EP  - 339
SP  - 334
PB  - Institute of Electrical and Electronics Engineers Inc.
A1  - Hassan, S.
A1  - Jaafar, J.
A1  - Khanesar, M.A.
A1  - Khosravi, A.
SN  - 9781509051342
KW  - Computer circuits; Forecasting; Fuzzy logic; Information science; Knowledge acquisition; Learning systems; Optimization
KW  -  Artificial bee colony optimizations; Data set; Extreme learning machine; Forecasting performance; Hybrid learning algorithm; Interval type-2 fuzzy; Interval type-2 fuzzy logic systems; Optimal parameter
KW  -  Learning algorithms
Y1  - 2016///
TI  - Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
N1  - cited By 3; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433
AV  - none
ER  -