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首页> 外文期刊>International Journal of Control >The fundamental closed-form solution of control-related states of kth order S3PR system with left-side non-sharing resource places of Petri nets
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The fundamental closed-form solution of control-related states of kth order S3PR system with left-side non-sharing resource places of Petri nets

机译:具有Petri网的左侧非共享资源位置的k阶S3PR系统控制相关状态的基本闭式解

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Due to the state explosion problem, it has been unimaginable to enumerate reachable states for Petri nets. Chao broke the barrier earlier by developing the very first closed-form solution of the number of reachable and other states for marked graphs and the kth order system. Instead of using first-met bad marking, we propose the moment to launch resource allocation' (MLR) as a partial deadlock avoidance policy for a large, real-time dynamic resource allocation system. Presently, we can use the future deadlock ratio of the current state as the indicator of MLR due to which the ratio can be obtained real-time by a closed-form formula. This paper progresses the application of an MLR concept one step further on Gen-Left kth order systems (one non-sharing resource place in any position of the left-side process), which is also the most fundamental asymmetric net structure, by the construction of the system's closed-form solution of the control-related states (reachable, forbidden, live and deadlock states) with a formula depending on the parameters of k and the location of the non-sharing resource. Here, we kick off a new era of real-time, dynamic resource allocation decisions by constructing a generalisation formula of kth order systems (Gen-Left) with r(*) on the left side but at arbitrary locations.
机译:由于状态爆炸问题,难以枚举Petri网的可达状态。 Chao通过为标记图和k阶系统开发可到达状态和其他状态数的第一个封闭形式解决方案,打破了障碍。我们建议使用启动“资源分配”(MLR)的时间,而不是使用第一时间差的坏标记,这是针对大型实时动态资源分配系统的部分死锁避免策略。目前,我们可以使用当前状态的未来死锁比率作为MLR的指标,因此可以通过闭式公式实时获得该比率。本文进一步将MLR概念在Gen-Left k阶系统(左侧过程中任何位置的一个非共享资源位置)上的应用进一步发展,该系统也是最基本的不对称网络结构与控制相关状态(可达,禁止,活动和死锁状态)的系统封闭式解决方案,其公式取决于k的参数和非共享资源的位置。在这里,我们通过构造在左侧但任意位置具有r(*)的k阶系统(Gen-Left)的泛化公式,开启了实时,动态资源分配决策的新时代。

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