Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters.

Discrete event dynamic systems Pub Date : 2019-01-01 Epub Date: 2019-08-22 DOI:10.1007/s10626-019-00287-9
Jorge Júlvez, Stephen G Oliver
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引用次数: 4

Abstract

The modeling of dynamic systems is frequently hampered by a limited knowledge of the system to be modeled and by the difficulty of acquiring accurate data. This often results in a number of uncertain system parameters that are hard to incorporate into a mathematical model. Thus, there is a need for modeling formalisms that can accommodate all available data, even if uncertain, in order to employ them and build useful models. This paper shows how the Flexible Nets (FNs) formalism can be exploited to handle uncertain parameters while offering attractive analysis possibilities. FNs are composed of two nets, an event net and an intensity net, that model the relation between the state and the processes of the system. While the event net captures how the state of the system is updated by the processes in the system, the intensity net models how the speed of such processes is determined by the state of the system. Uncertain parameters are accounted for by sets of inequalities associated with both the event net and the intensity net. FNs are not only demonstrated to be a valuable formalism to cope with system uncertainties, but also to be capable of modeling different system features, such as resource allocation and control actions, in a facile manner.

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柔性网络:具有不确定参数的动态系统的建模形式。
动态系统的建模经常受到待建模系统知识有限和难以获得准确数据的阻碍。这通常会导致许多不确定的系统参数,这些参数很难纳入数学模型。因此,为了使用它们并构建有用的模型,需要能够容纳所有可用数据(即使不确定)的建模形式化。本文展示了如何利用柔性网络(FNs)形式来处理不确定参数,同时提供有吸引力的分析可能性。神经网络由两个网络组成,一个是事件网络,一个是强度网络,这两个网络对系统的状态和过程之间的关系进行了建模。当事件网捕获系统中的进程如何更新系统状态时,强度网模拟了这些进程的速度如何由系统状态决定。不确定参数由与事件网和强度网相关的不等式集来解释。FNs不仅被证明是处理系统不确定性的一种有价值的形式,而且能够以一种方便的方式建模不同的系统特征,例如资源分配和控制行为。
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