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随机时间影响网络建模方法--扩展时间影响网络的确定性时间延迟 被引量：5

A new timed influence net model with stochastic delay
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摘要 基于时间影响网络的作战效能评估方法得到了广泛应用.但是传统方法无法有效刻画和处理作战效果产生和传播的时间延迟不确定性,从而难以全面支持作战方案对作战意图达成效果的评估.针对时间影响网络中时间延迟确定性假设的局限,引入随机时间延迟和随机信度序列两个模型参数,形成了一种新的时间影响网络——随机时间影响网络,给出了随机时间影响网络的数学描述和信度传播算法.通过算例演示了随机时间影响网络建模的一般过程,验证了信度传播算法的有效性. Timed influence net （TIN） is a powerful formalism to model uncertain causal relationship, and has been widely used in effectiveness evaluation of military operation alternatives. Stochastic timed influence net （STIN） is proposed to enhance TIN formalism by relaxing the constant delay assumption. Two key parameters, stochastic delay and stochastic belief sequence, are introduced in STIN to evaluate the effectiveness of alternatives under uncertain delays in the generation and propagation of operational effect. The mathematical description of and belief propagation algorithm in STIN is discussed in detail. This paper presents an example to illustrate the modeling process of STIN and the effectiveness of the belief propagation algorithm.
作者 朱延广 朱一凡 雷永林 曹星平
机构地区 国防科技大学信息系统与管理学院
出处 《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2012年第8期1814-1825,共12页 Systems Engineering-Theory & Practice
基金 国家自然科学基金(91024015,61074107)
关键词 随机时间影响网络 随机时间延迟 随机信度序列 因果关系建模 信度传播算法 stochastic timed influence nets stochastic delay stochastic belief sequence causal relationship model belief propagation algorithm
分类号 TP391.9 [自动化与计算机技术—计算机应用技术]
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参考文献19

  • 1The joint chiefs of staff U.S. joint doctrine for targeting[S]. Joint Publication 3-60, 2002.
  • 2Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference[M]. San Mateo. CA. Morgan Kaufmann, 1988.
  • 3Cooper G F. The computation complexity of probabilistic inference using Bayesian belief networks[J]. Artificial Intelligence, 1990, 42: 393-405.
  • 4Dagum P, Luby M. Approximating probabilistic inference in Bayesian belief networks Is Np-hard[J]. Artificial Intelligence, 1993, 60: 14-153.
  • 5Rosen J A, Smith W L. Influence net modeling with causal strengths: An evolutionary approach[C]//Proceedings of the 1996 Command and Control Research and Technology Symposium, Monterey CA, 1996.
  • 6Murphy K. Dynamic Bayesian networks: Representation, inference and learning[D]. UC Berkley, 2002.
  • 7Wagenhals L W, Shin I, Levis A H. Creating executable models of influence nets with coloured Petri netsIJ]. International Journal of STTT, Spring-Verlag, 1998, 2(2): 168-181.
  • 8Haider S, Levis A H. A heuristic approach for belief updating in timed influence nets[C]// Proceedings of 2004 Command and Control Research and Technology Symposium, Sail Deigo, 2004.
  • 9Wagenhals L W. Influence nets and Bayesian net approaches for course of action[R]. Adversary Behavioral Modeling Maxwell AFB, Montgomery AL, 2008.
  • 10Wagenhals L W, Levis A H. Course of action analysis in a cultural landscape using influence nets[C]//Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, Honolulu, 2007.

同被引文献41

  • 1龙德,么健石,徐心和.铁水运输系统智能仿真模型[J].东北大学学报（自然科学版）,2005,26(6):523-526. 被引量：9
  • 2殷瑞钰.钢铁制造过程的多维物流控制系统[J].金属学报,1997,33(1):29-38. 被引量：50
  • 3MURPHY K P. Dynamic Bayesian networks:Representa- tion, inference and learning[ D ]. Berkeley:California Uni- versity, 2000.
  • 4ROSEN J A, SMITH W L. Influence net modeling with causal strengths: An evolutionary approach [ C ]//Proceed- ings of Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, CA, 1996: 699 -708.
  • 5HAIDER S, LEVIS A H. A heuristic approach for beliefupdating in timed influence nets [ C]//Proceedings of 2004 Command and Control Research and Technology Symposium, San Deigo, 2004.
  • 6HAIDER S, LEVIS A H. Finding effective course of action using particle swarm optimization[ C ]//IEEE Congress on Evolutionary Computation, 2008 : 1135-1150.
  • 7ZHU Y G, LEI Y L. Stochastic timed influence nets [ C ]// Proceedings of the IEEE 2010 International Conference on Computer Application and System Modeling, Taiyuma, 2010:634-638.
  • 8ZAIDI A K, PAPANTONI T P. Theory of influence net- works [ J ]. Journal of Intelligent & Robotic Systems, 2010, 60(4) :457-491.
  • 9Murphy K P. Dynamic Bayesian networks= representation, in- ference and learning[D]. Berkeleyz California University, 2000.
  • 10Rosen J A, Smith W L. Influence net modeling with causal strengths: an evolutionary approach[C]//Proc, of the Com- mand and Control Research and Technology Symposium, 1996: 699 - 708.

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系统工程理论与实践
2012年 第8期

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