Petri网化简技术的动态特性研究

对于大而复杂的Petri网,“节点过多,状态爆炸”一直是阻碍其技术发展的主要问题,因此,如何有效地简化Petri网一直是Petri网研究领域的一个研究热点。将Petri网简化为若干子网是一个很有效的方法。提出了库所指标概念,根据该指标,给出了Petri网的分解定义,并给出了相关算法。在此基础上,依据所给出的算法研究化简技术的动态特性,将子网与原网的特性对应起来进行研究,为复杂系统的Petri网的分析提供了一种有效的途径。     

流体随机Petri网与混合Petri网的比较分析

流体随机Petri网和混合Petri网作为传统Petri网的拓展,可以建模离散成分和连续成分并存的混合系统,也可用于解决离散模型的状态空间爆炸问题.由于这两种建模机制远未发展成熟,分析比较它们的异同点有助于机制本身的进一步发展完善,有助于为特定的应用选择合适的建模方法.本文讨论了流体随机Petri网和混合Petri网的定义、连续标识、分析方法、以及相互转换的可能性.结论是这两种建模机制是互补的,各自适合于特定的应用场合.     

面向对象Petri网建模技术综述

阐述了面向对象技术和Petri网的结合方式,提出了面向对象Petri网的建模过程,给出其详细的建模步骤和流程图,讨论了面向对象Petri网的动态特性分析方法,分析和总结了OOPN,OOCPN、OOAPN以及OOTPN的研究现状和各自建模的利弊,最后对面向对象Petri网的相关技术和发展趋势进行了综述与展望.     

分布式数据库并发控制的Petri网分析

1 引言 Petri网是一种系统模拟和系统分析的工具,能够深刻和简洁地描述动态系统、分布式系统的特性和网络竞争、网络碰撞以及网络阻塞等各种现象。这在许多领域中已得到了广泛的应用。利用Petri网理论来分析和模拟分布式数据库系统,可以充分发挥Petri网的优点,形象、透彻地分析清楚分布式数据库实现的关键技术,这有助于分布式数据库系统的实现。     

Petri网时间特性分析

为了进一步研究随机Petri网的时间特性，引入了时间Petri网、闭网、常返网、有环网、无环网等概念，证明了随机Petri网是时间特性网的充要条件是其闭网为常返网，并给出了判别一个网是否是时间特性网的数值方法。同时讨论了变迁发射时间特性，给出了发射时间密度公式。     

基于Petri网模型的典型技术及其应用

Petri网是一种应用非常广泛的建模工具。本文简要介绍了Petri网的基本原理、分类和结构特点,并在此基础上,对Petri网技术的典型应用范围作了探讨。     

Petri网的对偶性质

位置/变迁(P/T)网是一个常用的Petri网类,它具有动态、并发和图形直观性等良好特性。因此,这类Petri网作为系统模拟与分析的有效工具已在众多领域中得到广泛应用。但对于大系统的分析也是Petri网方法遇到的一个难题。目前处理方法一般有两种:一是Petri网化简方法,即在保持网的某些性质不变的前提下,将一个复杂Petri网化为较简单的Petri网;二是通过一些较为简单的小网,利用某种运算或组合而得到较为复杂的大网,且在组合过程中,保持网的某些性质不变。但对Petri网的某些结构性质如活性,即使     

基于随机对象Petri网的柔性制造系统性能分析

为了增强传统对象Petri网的定量分析能力,提出随机对象Petri网模型。该模型具备随机性和层次特性,获得与随机Petri网的等价关系,从宏观和微观2个层面对系统进行性能分析,并将该模型应用到柔性制造系统中。实验结果表明,该系统保留了面向对象的建模能力,具有较强的定量分析能力。     

基于延迟特性的改进随机Petri网建模与分析

随机Petri网能够分析过程中的随机特性,但是不能有效地描述延迟和花费问题.为了解决上述问题,提出了一种基于延迟特性的随机Petri网模型.该模型能根据延迟时间和过程的随机性得到总的时间和花费特性.给出了状态类延迟时间和延迟花费的串联及并联化简方法.最后,通过首都机场的实例说明该方法能更好地表示出应急救援的时间和花费特性.     

Petri网死锁的分析与检测

本文在讨论Petri网静态结构和动态运行特性的基础上,给出了死锁的定义,描述了死锁的物理意义,并且给出了死锁的分析方法和检测算法.本文的研究对Petri网的工程应用具有基础性的重要作用.     

Detection of Ada static deadlocks using Petri net invariants

A method is presented for detecting deadlocks in Ada tasking programs using structural; and dynamic analysis of Petri nets. Algorithmic translation of the Ada programs into Petri nets which preserve control-flow and message-flow properties is described. Properties of these Petri nets are discussed, and algorithms are given to analyze the nets to obtain information about static deadlocks that can occur in the original programs. Petri net invariants are used by the algorithms to reduce the time and space complexities associated with dynamic Petri net analysis (i.e. reachability graph generation)     

Petri nets as a framework for the reconstruction and analysis of signal transduction pathways and regulatory networks

Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we exemplify how molecular mechanisms, biochemical or genetic, can be consistently respresented in the form of place/transition Petri nets. We then describe the application of Petri nets to the reconstruction of molecular and genetic networks from experimental data and their power to represent biological processes with arbitrary degree of resolution of the subprocesses at the cellular and the molecular level. Petri nets are executable formal language models that permit the unambiguous visualization of regulatory mechanisms, and they can be used to encode the results of mathematical algorithms for the reconstruction of causal interaction networks from experimental time series data.     

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

A characterization of the stochastic process underlying astochastic Petri net

Stochastic Petri nets (SPN's) with generally distributed firing times can model a large class of systems, but simulation is the only feasible approach for their solution. We explore a hierarchy of SPN classes where modeling power is reduced in exchange for an increasingly efficient solution. Generalized stochastic Petri nets (GSPN's), deterministic and stochastic Petri nets (DSPN's), semi-Markovian stochastic Petri nets (SM-SPN's), timed Petri nets (TPN's), and generalized timed Petri nets (GTPN's) are particular entries in our hierarchy. Additional classes of SPN's for which we show how to compute an analytical solution are obtained by the method of the embedded Markov chain (DSPN's are just one example in this class) and state discretization, which we apply not only to the continuous-time case (PH-type distributions), but also to the discrete case     

Automatic Parameterisation of Stochastic Petri Net Models of Biological Networks

Stochastic simulations are able to capture the fine grain behaviour and randomness of outcome of biological networks not captured by deterministic techniques. As such they are becoming an increasingly important tool in the biological community. However, current efforts in the stochastic simulation of biological networks are hampered by two main problems: firstly the lack of complete knowledge of kinetic parameters; and secondly the computational cost of the simulations. In this paper we investigate these problems using the framework of stochastic Petri nets. We present a new stochastic Petri net simulation tool NASTY which allows large numbers of stochastic simulations to be carried out in parallel. We then begin to address the important problem of incomplete knowledge of kinetic parameters by developing a distributed genetic algorithm, based on NASTY's simulation engine, to parameterise stochastic networks. Our algorithm is able to successfully estimate kinetic parameters to replicate a system's behaviour and we illustrate this by presenting a case study in which the kinetic parameters are derived for a stochastic model of the stress response pathway in the bacterium E.coli.     

时间Petri网的随机模拟

模拟是Peri网进行系统分析的常用方法之一。由于时间Petri网采用时间区间来描述变迁实施的时间范围,因此变迁的实施时间点在区间内是不确定的。提出了时间Petri网的随机模拟方法。该方法在变迁开始使能时,根据某种随机分布确定实施区间内的实施时间点;然后基于模拟仿真的实验数据,运用统计分析方法及算法,构造时间Petri网状态类树,计算变迁实施区间及实施概率,为时间Petri网的系统模拟提供了一种新的探索途径。     

共享合成Petri网的性质分析

组合Petri网的性质不变性对大型合成系统分析有着重要意义,从分析共享合成Petri 网的结构性质入手,分析了它的活性性质,给出了它的一个较为简单的活性判定的充要条件. 在此基础上,研究了共享合成Petri网的活性与行为不变性和状态不变性的关系,为系统合 成的活性和一致性的关系提供了分析依据.     

Petri net models for the semi-automatic construction of large scale biological networks

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. During the last 15 years, Petri nets have attracted more and more attention to help to solve this key problem. Regarding the published papers, it seems clear that hybrid functional Petri nets are the adequate method to model complex biological networks. Today, a Petri net model of biological networks is built manually by drawing places, transitions and arcs with mouse events. Therefore, based on relevant molecular database and information systems biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the application of Petri nets for modeling and simulation of biological networks. Furthermore, we will present a type of access to relevant metabolic databases such as KEGG, BRENDA, etc. Based on this integration process, the system supports semi-automatic generation of the correlated hybrid Petri net model. A case study of the cardio-disease related gene-regulated biological network is also presented. MoVisPP is available at .