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

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...
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Autor*in:	Mohammed A. A. Al-qaness [verfasserIn] Mohamed Abd Elaziz [verfasserIn] Ahmed A. Ewees [verfasserIn] Xiaohui Cui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	oil consumption ANFIS Multi-verse Optimizer forecasting

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 8(2019), 10, p 1071
Übergeordnetes Werk:	volume:8 ; year:2019 ; number:10, p 1071

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics8101071

Katalog-ID:	DOAJ084600845

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allfieldsSound	10.3390/electronics8101071 doi (DE-627)DOAJ084600845 (DE-599)DOAJ374d95a8712c4534b3b94b34be42a87a DE-627 ger DE-627 rakwb eng TK7800-8360 Mohammed A. A. Al-qaness verfasserin aut A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. oil consumption ANFIS Multi-verse Optimizer forecasting Electronics Mohamed Abd Elaziz verfasserin aut Ahmed A. Ewees verfasserin aut Xiaohui Cui verfasserin aut In Electronics MDPI AG, 2013 8(2019), 10, p 1071 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:8 year:2019 number:10, p 1071 https://doi.org/10.3390/electronics8101071 kostenfrei https://doaj.org/article/374d95a8712c4534b3b94b34be42a87a kostenfrei https://www.mdpi.com/2079-9292/8/10/1071 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2019 10, p 1071
language	English
source	In Electronics 8(2019), 10, p 1071 volume:8 year:2019 number:10, p 1071
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topic_facet	oil consumption ANFIS Multi-verse Optimizer forecasting Electronics
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authorswithroles_txt_mv	Mohammed A. A. Al-qaness @@aut@@ Mohamed Abd Elaziz @@aut@@ Ahmed A. Ewees @@aut@@ Xiaohui Cui @@aut@@
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callnumber-first	T - Technology
author	Mohammed A. A. Al-qaness
spellingShingle	Mohammed A. A. Al-qaness misc TK7800-8360 misc oil consumption misc ANFIS misc Multi-verse Optimizer misc forecasting misc Electronics A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
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topic_title	TK7800-8360 A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting oil consumption ANFIS Multi-verse Optimizer forecasting
topic	misc TK7800-8360 misc oil consumption misc ANFIS misc Multi-verse Optimizer misc forecasting misc Electronics
topic_unstemmed	misc TK7800-8360 misc oil consumption misc ANFIS misc Multi-verse Optimizer misc forecasting misc Electronics
topic_browse	misc TK7800-8360 misc oil consumption misc ANFIS misc Multi-verse Optimizer misc forecasting misc Electronics
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title	A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
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title_full	A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
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title_sort	modified adaptive neuro-fuzzy inference system using multi-verse optimizer algorithm for oil consumption forecasting
callnumber	TK7800-8360
title_auth	A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
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A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

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Zugang & Verfügbarkeit

Vorhandene Bände

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