期刊文献+

故障诊断专家系统的可拓知识表示和匹配研究 被引量:4

Study on Extension Knowledge Representation and Matching of Fault Diagnosis Expert System
下载PDF
导出
摘要 知识表示和匹配是设计专家系统的核心问题;首先通过引入基元理论,实现了产生式、语义网络、框架和案例的基元表示,建立了故障诊断专家系统的可拓知识表示模型,该模型包括激励基元、测量基元和结论基元3个部分;然后提出了先匹配激励基元再匹配测量基元的可拓知识匹配步骤,并构建了属性精确值和区间值混合的匹配度计算公式;最后以某型大气数据计算机的测试数据为例,与建立的可拓知识进行匹配,诊断结果与实际情况相符。 Knowledge representation and matching are key problems of expert system. By inducting basic--element theory, the basic-- element representation of production, semantic network, frame and case are fulfilled, and extension knowledge representation model of diag- nostic expert system is set up, which contains source, test and result three basic--element. The matching process needs two steps, first match source basic--element, second match test basic--element. At last, the test data of an air data computer is used to match the extension knowledge. The results are consistent with the fact.
出处 《计算机测量与控制》 北大核心 2014年第6期1670-1672,1686,共4页 Computer Measurement &Control
基金 总装武器装备预研基金项目(9140A27020212JB14311)
关键词 可拓 故障诊断 专家系统 知识表示 知识匹配 extension fault diagnosis expert system knowledge representat!on knowledge matching
  • 相关文献

参考文献6

二级参考文献28

共引文献65

同被引文献35

  • 1陈文伟,杨春燕,黄金才.可拓知识与可拓知识推理[J].哈尔滨工业大学学报,2006,38(7):1094-1096. 被引量:31
  • 2LI Jie SHEN Shi-tuan.Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System[J].Chinese Journal of Aeronautics,2007,20(3):223-229. 被引量:6
  • 3Sammut C A, Webb G I, Behavioral Cloning, Encyclopaedia of Ma- chine Learning [M]. edn. 1st edition, Springer, New York, 2010: 93-97.
  • 4Pastor P, Hoffmann H, et all Learning and generalization of motor skills by learning from demonstration [A]. Robotics and Automa- tion, 2009. ICRA'09. IEEE International Conference [C] . IEEE, 2009: 763-768. conference on artifical intelligence [C] . Pasade-.
  • 5Gergely Neu and Csaba Szepesvari. Apprenticeship learning using inverse reinforcement Learning and gradient methods [A]. In Pro ceedings of the 23rd Conference on Uncertainty in Artificial Intelli gence (UAI) [C] . Vancouver, BC, Canada, 2007: 295-302.
  • 6Jaedeug Choi, Kee-Eung Kim, Inverse reinforcement learning in partially observable environments [A]. Proceedings of the 21st in- ternational joint na, California, USA, 2009: 1028-1033.
  • 7Morimura T, E. Uchibe, Yoshimoto J, Peters et al. Derivatives of logarithmic stationary distributions for policy gradient rein{orcement learning [J], Neural Comput. , 2010, 22 (2): 342 - 376.
  • 8Kim H J, Jordan M I, Sastry S, et al. Autonomous helicopter flight via reinforcement learning C. Advances in neural informa- tion processing systems 2003: 119- 123.
  • 9Caspi Y, Irani M. Feature- based sequence- to- sequence matc- hing [J], International Journal of Computer Vision, 2006, 68 (1) : 53 - 64.
  • 10Black M J, Jepson A D. A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions. Computer Vision ECCV'98 [M . Springer Ber- lin Heidelberg, 1998 62-68.

引证文献4

二级引证文献9

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部