Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS
Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checki...
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| Autori principali: | , |
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| Natura: | Articolo |
| Lingua: | inglese |
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Tikrit University
2019-03-01
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| Serie: | Tikrit Journal of Pure Science |
| Soggetti: | |
| Accesso online: | https://tjpsj.org/index.php/tjps/article/view/339 |
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| Riassunto: | Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.
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| ISSN: | 1813-1662 2415-1726 |