{"title":"自适应神经模糊推理系统预测石油产量","authors":"Morteza Saberi, A. Azadeh, S. Ghorbani","doi":"10.1109/ISIE.2008.4676919","DOIUrl":null,"url":null,"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.","PeriodicalId":262939,"journal":{"name":"2008 IEEE International Symposium on Industrial Electronics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting oil production by adaptive neuro fuzzy inference system\",\"authors\":\"Morteza Saberi, A. Azadeh, S. Ghorbani\",\"doi\":\"10.1109/ISIE.2008.4676919\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":262939,\"journal\":{\"name\":\"2008 IEEE International Symposium on Industrial Electronics\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Industrial Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIE.2008.4676919\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Industrial Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIE.2008.4676919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting oil production by adaptive neuro fuzzy inference system
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.
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