{"title":"子网络时间分布作为广义随机Petri网多层评价的手段","authors":"G. Klas, Reinhard Matuschka","doi":"10.1109/PNPM.1991.238783","DOIUrl":null,"url":null,"abstract":"A new hierarchical evaluation procedure for generalized stochastic Petri nets (GSPN) is being presented. It is based on extensions of flow equivalent aggregation (FEA). At every level of hierarchy, subnets are approximated by substitute networks. As criterion for the similarity of the networks, the subnetwork time distribution (STD) is used which is the sojourn time distribution of a token X in a subnet conditioned on the token distribution at the epoch of arrival of X and the context into which the net is embedded for determining the STD. The computation of a substitute network's type and parameters is outlined. The performance of this technique called FEAD (FEA based on subnetwork time distribution) is discussed by means of examples.<<ETX>>","PeriodicalId":137470,"journal":{"name":"Proceedings of the Fourth International Workshop on Petri Nets and Performance Models PNPM91","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Subnetwork time distributions as a means for multilevel evaluation of generalized stochastic Petri nets\",\"authors\":\"G. Klas, Reinhard Matuschka\",\"doi\":\"10.1109/PNPM.1991.238783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new hierarchical evaluation procedure for generalized stochastic Petri nets (GSPN) is being presented. It is based on extensions of flow equivalent aggregation (FEA). At every level of hierarchy, subnets are approximated by substitute networks. As criterion for the similarity of the networks, the subnetwork time distribution (STD) is used which is the sojourn time distribution of a token X in a subnet conditioned on the token distribution at the epoch of arrival of X and the context into which the net is embedded for determining the STD. The computation of a substitute network's type and parameters is outlined. The performance of this technique called FEAD (FEA based on subnetwork time distribution) is discussed by means of examples.<<ETX>>\",\"PeriodicalId\":137470,\"journal\":{\"name\":\"Proceedings of the Fourth International Workshop on Petri Nets and Performance Models PNPM91\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth International Workshop on Petri Nets and Performance Models PNPM91\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PNPM.1991.238783\",\"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 the Fourth International Workshop on Petri Nets and Performance Models PNPM91","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PNPM.1991.238783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
提出了一种新的广义随机Petri网(GSPN)分级评价方法。它是基于流动等效聚集(FEA)的扩展。在每一层次结构中,子网都是由替代网络近似表示的。采用子网时间分布(STD)作为判定网络相似性的标准,即令牌X在子网中的停留时间分布,该时间分布取决于令牌X到达时间点的分布和网络所处的环境,并给出了替代网络类型和参数的计算方法。通过实例讨论了基于子网时间分布的FEAD (FEA based on subnetwork time distribution)技术的性能
Subnetwork time distributions as a means for multilevel evaluation of generalized stochastic Petri nets
A new hierarchical evaluation procedure for generalized stochastic Petri nets (GSPN) is being presented. It is based on extensions of flow equivalent aggregation (FEA). At every level of hierarchy, subnets are approximated by substitute networks. As criterion for the similarity of the networks, the subnetwork time distribution (STD) is used which is the sojourn time distribution of a token X in a subnet conditioned on the token distribution at the epoch of arrival of X and the context into which the net is embedded for determining the STD. The computation of a substitute network's type and parameters is outlined. The performance of this technique called FEAD (FEA based on subnetwork time distribution) is discussed by means of examples.<>