Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (12): 3262-3268.DOI: 10.11772/j.issn.1001-9081.2016.12.3262

Previous Articles     Next Articles

System dynamics relevancy analysis model based on stochastic function Petri-net

HUANG Guangqiu, HE Tong, LU Qiuqin   

  1. School of Management, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
  • Received:2016-07-06 Revised:2016-08-30 Online:2016-12-10 Published:2016-12-08
  • Supported by:
    This work is partially supported by the General Project of Humanity and Social Science Programming Foundation of Chinese Ministry of Education (15YJA910002), the Key Project-Basic Research Project of Natural Science of Shaanxi Province (2015JZ010), the Industrialization Project of Shaanxi Provincial Department of Education (16JF015), the Social Science Foundation of Shaanxi Province (2014P07), the Soft Science Research Project of Bureau of Science and Technology of Xi'an Municipality (SF1505(9)).


黄光球, 何通, 陆秋琴   

  1. 西安建筑科技大学 管理学院, 西安 710055
  • 通讯作者: 黄光球
  • 作者简介:黄光球(1964-),男,湖南桃源人,教授,博士,主要研究方向:Petri网、系统动力学、群智能算法、计算机模拟;何通(1994-),男,内蒙古包头人,硕士研究生,主要研究方向:Petri网:陆秋琴(1966-),女,广西武鸣人,教授,博士,主要研究方向:Petri网、群智能算法、数值模拟。
  • 基金资助:

Abstract: There are several problems that the System Dynamics (SD) model cannot express both random delay and conditional transitions between different states, and the Stochastic Petri-Net (SPN) itself still has the defect of insufficient computing ability. In order to solve the problems, firstly, the SPN was expanded and the Stochastic Function Petri-Net (SFPN) model was proposed. Secondly, combining SFPN with SD, the SFPN-SD model was put forward. Because the transits in SFPN could be used to accurately describe random delay, therefore, the first problems in SD model was solved. Because the conditions arcs in SFPN could be used to express the conditional transferring among places, as a result, the second problem in SD was solved. Finally, some state variables and state transition equations were appended in places and transitions of SPN, while these state variables and state transition equations were the different interpretations of level, auxiliary and rate variables as well as level and rate equations in SD model. The state transition equations could realize complicated computations, and thus the problem of insufficient computing ability in SPN was solved. The SFPN-SD model inherited all the features of the SD model, at the same time, all the features of SPN were incorporated into the SFPN-SD model. Compared with the SD model, the proposed SFPN-SD model has such advantages that system states and the meaning of their types as well as the process of state evolution become more clear. And the system's dynamic changes are driven by events in SFPN-SD, which it can describe autonomous dynamic stochastic evolution of complex system more realistically. The case studies show that, compared with the SD model, the proposed SFPN-SD model has stronger, more comprehensive abilities such as relevancy analysis description and simulation of complex system.

Key words: Petri-net, Stochastic Petri-Net (SPN), system dynamics, system simulation, relevancy analysis

摘要: 针对系统动力学(SD)模型既无法表达延迟具有随机性,又无法描述不同状态之间存在的条件转移,以及随机Petri网(SPN)本身存在计算能力不足等问题,首先,将SPN进行扩展,提出了随机函数Petri网(SFPN)模型;然后,将SFPN与SD相结合,提出了一种SFPN-SD模型。因SFPN模型中的变迁本身能精确描述随机延迟,故解决了SD模型存在的第一个问题;因SFPN模型中的条件弧能表达库所之间的有条件转移,故解决了SD模型存在的第二个问题;最后,在SPN的库所和变迁中定义一些状态变量及其状态转移方程,而状态变量及其状态转移方程就是SD模型中的水平变量、辅助变量、速率变量、水平方程和速率方程的不同解释,状态转移方程可以实现复杂的计算,于是解决了SPN模型的计算能力不足的问题。SFPN-SD模型很好地继承了SD模型的全部特征,同时又将随机Petri网的全部特征融入到SFPN-SD模型中。与SD模型相比,SFPN-SD模型具有系统的状态及其类型的含义更明确、状态演变过程更明确的特点,且其描述的系统变化动态性是通过事件激发的,从而更逼真地描述了复杂系统的自主动态随机演变行为。实例研究表明,SFPN-SD模型比SD模型具有更强、更全面的对复杂系统的描述关联分析与模拟能力。

关键词: Petri网, 随机Petri网, 系统动力学, 系统模拟, 关联分析

CLC Number: