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Open AccessArticle

A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

1
School of Computer Science, Wuhan University, Wuhan 430072, China
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Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
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Department of Computer, Damietta University, Damietta 34511, Egypt
4
School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(10), 1071; https://doi.org/10.3390/electronics8101071
Received: 22 August 2019 / Revised: 16 September 2019 / Accepted: 18 September 2019 / Published: 21 September 2019
(This article belongs to the Special Issue Fuzzy Systems and Data Mining)
Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems. View Full-Text
Keywords: oil consumption; ANFIS; Multi-verse Optimizer; forecasting oil consumption; ANFIS; Multi-verse Optimizer; forecasting
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Al-qaness, M.A.A.; Abd Elaziz, M.; Ewees, A.A.; Cui, X. A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting. Electronics 2019, 8, 1071.

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