期刊文献+

复杂条件下大型洞室群施工反馈分析集成智能系统研究 被引量:3

Research on integration intelligent system of feedback analysis for large cavern group under complicated conditions
下载PDF
导出
摘要 地下工程具有复杂性和不确定性的特点,大型洞室群施工反馈分析需要综合经验知识、统计分析和数值模拟等多种手段。在介绍反馈分析流程和多种反馈分析方法基础上,提出的包括数据库、知识库、模型库和推理机的 IDSS 框架的反馈分析的集成智能系统,并基于 PB 和 VC++平台进行了程序的初步开发。通过在水布垭电站地下厂房的施工反馈分析中应用表明,该系统模式和开发策略是可行的。 Underground engineering has characters of complexity and uncertainty,the feedback analysis for large cavern group construction needs combing several means such as experience knowledge,statistic analysis and numerical simulation.The variety methods and the process of feedback analysis are introduced,then the feedback analysis integration intelligence system is proposed based on IDSS frame including database,knowledge base,model base and reasoning machine;and the system has been developed by Power Builder and VC++.The system has been used'in the feedback analysis for the underground powerhouse of Shuibuya, Hydropower Station.The results show that the systematic pattern and developing method are feasible.
作者 姜谙男
出处 《岩土力学》 EI CAS CSCD 北大核心 2006年第S1期230-234,共5页 Rock and Soil Mechanics
基金 国家自然科学基金:(50508007) 中科院知识创新工程重要方向性项目:地下工程开发中的关键技术问题(KGCX2-SW-302-02)
关键词 大型洞室群 反馈分析 集成智能 智能决策支持系统 large cavern group feedback analysis integration intelligence intelligent decision support system
  • 相关文献

参考文献5

二级参考文献27

  • 1刘勇 康力山.非数值并行算法(第二册)——遗传算法[M].北京:科学出版社,1997..
  • 2亢会明.隧道围岩稳定性分析智能决策支持系统研究,重庆大学博士学位论文[M].重庆,2001..
  • 3Burge CJC. A tutorial on support vector machines for pattern recognition[J] .Data Mining and Knowledge Discovery, 1998, (2) :121 - 167.
  • 4Alex J Smola, Bernhard Schoelkopf. A Tutorial on Support Vector Regression[R]. NeuroCOLT2 Technical Report Series, 1998.
  • 5John C Platt. Sequeotial Minimal Optimization:A Fast Algorithm for training Support Vector machines[R].Technical Report,1998
  • 6van Gestel T, Suykens J A K, Lanckriet G, et al. Baye sian framework for least squares support vector machine classifiers, gaussian processes and kernel fisher discriminant analysis[J].Neural Computation,2002,15(5):1115-1148.
  • 7Mackay D J C. Bayesian interpolation[J].Neural Computation,1992,4(3):415-447.
  • 8Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions[J]. Neural Networks,1999,12(6):783-789.
  • 9Vapnik V N. An overview of statistical learning theory. IEEE Trans Neural Network, 1999,10(5):988-999.
  • 10Suykens J A K, Vandewalle J. Least square support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.

共引文献139

同被引文献18

引证文献3

二级引证文献16

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