{"title":"基于改进的灰色关联度 IOWHA 运算符的区间类型组合预测模型","authors":"Feng Xu, Xiaowei Cai","doi":"10.54097/fcis.v6i1.23","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy of interval number prediction, this paper proposes a new type of interval number combination prediction model based on improved grey correlation degree and IOWHA operator. Transform the interval number into the center and radius of equivalent information, introduce the IOWHA operator, use the interval number prediction accuracy as the inducing factor, and improve the grey correlation degree as the optimal criterion to construct a variable weight interval type combination prediction model based on the improved grey correlation degree and IOWHA operator. And the variable weight coefficient interval type combination prediction model is applied to the prediction of interval number sequences, and the example proves that the model is effective.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"62 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interval Type Combination Prediction Model based on Improved Grey Correlation Degree IOWHA Operator\",\"authors\":\"Feng Xu, Xiaowei Cai\",\"doi\":\"10.54097/fcis.v6i1.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the accuracy of interval number prediction, this paper proposes a new type of interval number combination prediction model based on improved grey correlation degree and IOWHA operator. Transform the interval number into the center and radius of equivalent information, introduce the IOWHA operator, use the interval number prediction accuracy as the inducing factor, and improve the grey correlation degree as the optimal criterion to construct a variable weight interval type combination prediction model based on the improved grey correlation degree and IOWHA operator. And the variable weight coefficient interval type combination prediction model is applied to the prediction of interval number sequences, and the example proves that the model is effective.\",\"PeriodicalId\":346823,\"journal\":{\"name\":\"Frontiers in Computing and Intelligent Systems\",\"volume\":\"62 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Computing and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54097/fcis.v6i1.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54097/fcis.v6i1.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interval Type Combination Prediction Model based on Improved Grey Correlation Degree IOWHA Operator
In order to improve the accuracy of interval number prediction, this paper proposes a new type of interval number combination prediction model based on improved grey correlation degree and IOWHA operator. Transform the interval number into the center and radius of equivalent information, introduce the IOWHA operator, use the interval number prediction accuracy as the inducing factor, and improve the grey correlation degree as the optimal criterion to construct a variable weight interval type combination prediction model based on the improved grey correlation degree and IOWHA operator. And the variable weight coefficient interval type combination prediction model is applied to the prediction of interval number sequences, and the example proves that the model is effective.