...
首页> 外文期刊>IEEE Transactions on Automatic Control >A lower bound for the correct subset-selection probability and its application to discrete-event system simulations
【24h】

A lower bound for the correct subset-selection probability and its application to discrete-event system simulations

机译:正确的子集选择概率的下限及其在离散事件系统仿真中的应用

获取原文
获取原文并翻译 | 示例
           

摘要

Ordinal optimization concentrates on finding a subset of good designs, by approximately evaluating a parallel set of designs, and reduces the required simulation time dramatically for discrete-event simulation and optimization. The estimation of the confidence probability (CP) that the selected designs contain at least one good design is crucial to ordinal optimization. However, it is very difficult to estimate this probability in DES simulation, especially for complicated DES with large number of designs. This paper proposes two simple lower bounds for quantifying the confidence probability. Numerical testing is presented.
机译:顺序优化专注于通过近似评估一组并行设计来寻找好的设计子集,并显着减少离散事件仿真和优化所需的仿真时间。所选设计包含至少一个好的设计的置信概率(CP)的估计对于顺序优化至关重要。但是,在DES仿真中很难估计这种可能性,尤其是对于具有大量设计的复杂DES而言。本文提出了两个简单的下界来量化置信概率。提出了数值测试。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号