{"title":"基于模糊模型的远程预测控制","authors":"J.V. de Oliveira, J. M. Lemos","doi":"10.1109/FUZZY.1994.343668","DOIUrl":null,"url":null,"abstract":"Fuzzy model based long-range predictive control algorithms are presented. A distributed fuzzy relational model is used for forecasting. The control law seeks to minimize a multi-step performance index using a receding horizon strategy. The adaptation mechanisms deal with the linguistic maintenance of the involved membership functions. A case study is presented.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Fuzzy model based long-range predictive control\",\"authors\":\"J.V. de Oliveira, J. M. Lemos\",\"doi\":\"10.1109/FUZZY.1994.343668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fuzzy model based long-range predictive control algorithms are presented. A distributed fuzzy relational model is used for forecasting. The control law seeks to minimize a multi-step performance index using a receding horizon strategy. The adaptation mechanisms deal with the linguistic maintenance of the involved membership functions. A case study is presented.<<ETX>>\",\"PeriodicalId\":153967,\"journal\":{\"name\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1994.343668\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1994.343668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy model based long-range predictive control algorithms are presented. A distributed fuzzy relational model is used for forecasting. The control law seeks to minimize a multi-step performance index using a receding horizon strategy. The adaptation mechanisms deal with the linguistic maintenance of the involved membership functions. A case study is presented.<>