{"title":"中国粮食生产的TEI@I灰色预测方法","authors":"Qiting Chen, Chao Zhang","doi":"10.1109/GSIS.2015.7301864","DOIUrl":null,"url":null,"abstract":"This paper adopts a novel methodology to predict China's grain production. Using a grey model to capture the main trend, this paper establishes a modified model of BP neural networks and then analyzes the irregular events and its influencing direction and degree with Delphi methods. By testing the validity of the final model, the result shows an encouraging conclusion that the model is effective and China's grain production will continue to increase in the next six years.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Grey prediction of China grain production with TEI@I methodology\",\"authors\":\"Qiting Chen, Chao Zhang\",\"doi\":\"10.1109/GSIS.2015.7301864\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper adopts a novel methodology to predict China's grain production. Using a grey model to capture the main trend, this paper establishes a modified model of BP neural networks and then analyzes the irregular events and its influencing direction and degree with Delphi methods. By testing the validity of the final model, the result shows an encouraging conclusion that the model is effective and China's grain production will continue to increase in the next six years.\",\"PeriodicalId\":246110,\"journal\":{\"name\":\"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSIS.2015.7301864\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2015.7301864","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grey prediction of China grain production with TEI@I methodology
This paper adopts a novel methodology to predict China's grain production. Using a grey model to capture the main trend, this paper establishes a modified model of BP neural networks and then analyzes the irregular events and its influencing direction and degree with Delphi methods. By testing the validity of the final model, the result shows an encouraging conclusion that the model is effective and China's grain production will continue to increase in the next six years.