Title :
Forecasting oil production by adaptive neuro fuzzy inference system
Author :
Saberi, Morteza ; Azadeh, Ali ; Ghorbani, Sara
Author_Institution :
Dept. of Ind. Eng., Azad Univ. of Tafresh, Tafresh
fDate :
June 30 2008-July 2 2008
Abstract :
In this paper, the efficiency of neuro fuzzy network (ANFIS) is examined against auto regression (AR). Mean absolute percentage error (MAPE) is applied for this purpose. After applying different data preprocessing methods, the models are developed. A method for calculating ANFIS performance is also proposed. Due to various seasonal and monthly changes in oil production and difficulties in modeling it with conventional methods, we consider a case study in four countries for oil production estimation. Finally, analysis of variance (ANOVA) and Duncan Multiple Range Test (DMRT) is conducted for each country to evaluate the most efficient method.
Keywords :
forecasting theory; fuzzy neural nets; inference mechanisms; petroleum industry; production engineering computing; statistical analysis; ANOVA; Duncan Multiple Range Test; adaptive neuro fuzzy inference system; analysis of variance; auto regression; data preprocessing methods; mean absolute percentage error; neuro fuzzy network; oil production forecasting; Adaptive systems; Analysis of variance; Biological neural networks; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Industrial engineering; Petroleum; Predictive models; Production systems; ANFIS; ANOVA; Oil Production Estimation; Time Series Analysis;
Conference_Titel :
Industrial Electronics, 2008. ISIE 2008. IEEE International Symposium on
Conference_Location :
Cambridge
Print_ISBN :
978-1-4244-1665-3
Electronic_ISBN :
978-1-4244-1666-0
DOI :
10.1109/ISIE.2008.4676919
