{"title":"用颗粒龙格-库塔方法建模动态过程","authors":"T. Co","doi":"10.1109/GrC.2007.100","DOIUrl":null,"url":null,"abstract":"By incorporating the Runge-Kutta methods with functions defined within the frameworks of multilayered granular domains, a nonlinear continuous-time dynamic process can be efficiently modeled. The several layers allow for the construction of models spanning different granular size to be used for applications that require different levels of precision and efficiency. In this paper, we discuss a particular implementation of this approach using multilinear interpolation functions.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Dynamic Processes Using Granular Runge-Kutta Methods\",\"authors\":\"T. Co\",\"doi\":\"10.1109/GrC.2007.100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By incorporating the Runge-Kutta methods with functions defined within the frameworks of multilayered granular domains, a nonlinear continuous-time dynamic process can be efficiently modeled. The several layers allow for the construction of models spanning different granular size to be used for applications that require different levels of precision and efficiency. In this paper, we discuss a particular implementation of this approach using multilinear interpolation functions.\",\"PeriodicalId\":259430,\"journal\":{\"name\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GrC.2007.100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Granular Computing (GRC 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GrC.2007.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling Dynamic Processes Using Granular Runge-Kutta Methods
By incorporating the Runge-Kutta methods with functions defined within the frameworks of multilayered granular domains, a nonlinear continuous-time dynamic process can be efficiently modeled. The several layers allow for the construction of models spanning different granular size to be used for applications that require different levels of precision and efficiency. In this paper, we discuss a particular implementation of this approach using multilinear interpolation functions.
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