{"title":"模糊过渡系统行为距离的计算","authors":"Tian-Ming Bu, Hengyang Wu, Yixiang Chen","doi":"10.1109/TASE.2017.8285626","DOIUrl":null,"url":null,"abstract":"The behavioural distance is a more robust way of formalising behavioural similarity between states than bisimulations. The smaller the distance, the more alike the states are. It is helpful for quantitative verifications of concurrent systems. The main contribution of this paper is an effective procedure for computing behavioural distance introduced by Cao et al. (IEEE Transactions on Fuzzy Systems, 21 (2013) 735–747). The time complexity of the algorithm is O(n5 m3 lg n), where n is the number of states and m is the number of transitions in the underlying transition systems. The key step in this algorithm is to compute the distance between two distributions, which is defined as the value of a mathematical programming problem (MP). In this process, some interesting properties about solutions of a fuzzy system, which is a constraint of the MP, are discussed.","PeriodicalId":221968,"journal":{"name":"2017 International Symposium on Theoretical Aspects of Software Engineering (TASE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computing behavioural distance for fuzzy transition systems\",\"authors\":\"Tian-Ming Bu, Hengyang Wu, Yixiang Chen\",\"doi\":\"10.1109/TASE.2017.8285626\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The behavioural distance is a more robust way of formalising behavioural similarity between states than bisimulations. The smaller the distance, the more alike the states are. It is helpful for quantitative verifications of concurrent systems. The main contribution of this paper is an effective procedure for computing behavioural distance introduced by Cao et al. (IEEE Transactions on Fuzzy Systems, 21 (2013) 735–747). The time complexity of the algorithm is O(n5 m3 lg n), where n is the number of states and m is the number of transitions in the underlying transition systems. The key step in this algorithm is to compute the distance between two distributions, which is defined as the value of a mathematical programming problem (MP). In this process, some interesting properties about solutions of a fuzzy system, which is a constraint of the MP, are discussed.\",\"PeriodicalId\":221968,\"journal\":{\"name\":\"2017 International Symposium on Theoretical Aspects of Software Engineering (TASE)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Symposium on Theoretical Aspects of Software Engineering (TASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TASE.2017.8285626\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Symposium on Theoretical Aspects of Software Engineering (TASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASE.2017.8285626","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
行为距离是一种比双模拟更可靠的形式化状态间行为相似性的方法。距离越小,状态越相似。它有助于并行系统的定量验证。本文的主要贡献是Cao等人引入的计算行为距离的有效程序(IEEE Transactions on Fuzzy Systems, 21(2013) 735-747)。算法的时间复杂度为O(n5 m3 lg n),其中n为底层迁移系统的状态数,m为底层迁移系统的迁移数。该算法的关键步骤是计算两个分布之间的距离,这被定义为一个数学规划问题(MP)的值。在此过程中,讨论了一类模糊系统解的一些有趣性质。
Computing behavioural distance for fuzzy transition systems
The behavioural distance is a more robust way of formalising behavioural similarity between states than bisimulations. The smaller the distance, the more alike the states are. It is helpful for quantitative verifications of concurrent systems. The main contribution of this paper is an effective procedure for computing behavioural distance introduced by Cao et al. (IEEE Transactions on Fuzzy Systems, 21 (2013) 735–747). The time complexity of the algorithm is O(n5 m3 lg n), where n is the number of states and m is the number of transitions in the underlying transition systems. The key step in this algorithm is to compute the distance between two distributions, which is defined as the value of a mathematical programming problem (MP). In this process, some interesting properties about solutions of a fuzzy system, which is a constraint of the MP, are discussed.