系统仿真学报 ›› 2020, Vol. 32 ›› Issue (2): 217-228.doi: 10.16182/j.issn1004731x.joss.18-0042

• 仿真支撑平台/系统技术 • 上一篇    下一篇

基于时间窗的SVDBN近似推理算法研究

陈海洋, 柴冰, 王瑞兰, 曹璐   

  1. 西安工程大学 电子信息学院,陕西 西安 710048
  • 收稿日期:2018-01-22 修回日期:2018-09-17 出版日期:2020-02-18 发布日期:2020-02-19
  • 作者简介:陈海洋(1967-),男,陕西西安,博士,副教授,研究方向为人工智能;柴冰(1994-),女,山西临汾,硕士生,研究方向为人工智能;王瑞兰(1995-),女,陕西榆林,硕士生,研究方向为人工智能。
  • 基金资助:
    国家自然科学基金(61573285)

Research on Approximate Reference Algorithm of SVDBN based on Sliding Window

Chen Haiyang, Chai Bing, Wang Ruilan, Cao Lu   

  1. School of Information and Electronics, Xi'an Polytechnic University, Xi'an 710048, China
  • Received:2018-01-22 Revised:2018-09-17 Online:2020-02-18 Published:2020-02-19

摘要: 变结构动态贝叶斯网络(SVDBN)在处理非稳态过程的不确定问题具有其独特的优越性。为克服SVDBN推理算法不能实现在线推理的缺陷,在引入SVDBN的时间窗和时间窗宽度概念基础上,阐述了基于时间窗的SVDBN在线近似推理机制,提出了2种在线近似推理算法,即基于时间窗的变结构离散动态贝叶斯网络(SVDDBN)递推推理算法和基于时间窗的SVDDBN快速推理算法。通过仿真实验验证了这2种算法的有效性,并从复杂度、适用范围及更新时间等方面进行了比较。

关键词: 变结构离散动态贝叶斯网络, 近似推理, 信息传播, 时间窗

Abstract: Structure-variable dynamic Bayesian networks (SVDBN) have the special advantage in dealing with the uncertainty of the unstable processes. In order to overcome the disadvantage that the inference algorithms of the SVDBN are unable to apply online, introducing the concepts of SVDBN sliding window and the window width, the online approximate inference mechanism of structure-variable dynamic Bayesian networks based on sliding window is explained, and two online algorithms are proposed, that is the recursive inference algorithm of structure-variable discrete dynamic Bayesian networks (SVDDBN) based on sliding window and the fast inference algorithm of SVDDBN based on sliding window. Experimental simulations show the effectiveness of the two inference algorithms and compare their complexity, application, updated time and so on.

Key words: structure-variable discrete dynamic Bayesian networks, approximate inference, information dissemination, sliding window

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