暂无标题

doi:10.16579/j.issn.1001.9669.2021.01.019

您当前的位置：

首页 >

文章列表页 >

暂无标题

更新时间：2022-09-22

- 暂无标题
- Journal of Mechanical Strength Issue 1, Pages: 129-136(2021)
- 作者机构：
  
  1. 中车株洲电力机车有限公司大功率交流传动电力机车系统集成国家重点实验室
  2. 西南交通大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2021.01.019
  CLC：
扫描看全文
[J]. Journal of Mechanical Strength (1):129-136(2021)
DOI：

[J]. Journal of Mechanical Strength (1):129-136(2021) DOI： 10.16579/j.issn.1001.9669.2021.01.019.

摘要

主动学习Kriging(ALK)模型能够只在极限状态平面附近的狭小区域近似功能函数,因此能够极大提高可靠性分析的效率。但是已有方法多数基于DACE工具箱建立Kriging模型,难以获得Kriging模型的最优相关参数,进而难以获得最优的训练点。引入差分进化算法,对Kriging模型的相关参数进行全局搜索,提高每次迭代Kriging模型的预测精度,保证每次学习都能获得最优的训练点,从而明显提高了基于ALK模型的可靠性分析方法的效率。

Abstract

Active learning Kriging（ ALK） model is able to only approximate the performance function in a narrow region around the limit state surface.Therefore,the efficiency of reliability analysis is remarkably improved.However,most of the existing strategies build the ALK model based on a so-called DACE toolbox.DACE cannot obtain the global optimal parameter of a Kriging model and the training point chosen in each iteration cannot be the optimal one.In this paper,one famous global optimization,i.e.,the differential evolution algorithm is introduced to explore the optimal parameter of Kriging model and improve the accuracy of Kriging prediction information.As a result,the training point in each iteration is guaranteed to be the global optimal one and the efficiency of ALK model is largely improved.

关键词

主动学习Kriging模型差分进化算法可靠性分析

Keywords

references

Views

349

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

RELIABILITY ANALYSIS ON HYBRID SURROGATE MODEL OF RADIAL BASIS FUNCTION AND SPARSE POLYNOMIAL CHAOS EXPANSION (MT)

RELIABILITY ANALYSIS OF INDUSTRIAL ROBOT USING T-S FUZZY FAULT TREE

STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK

STOCHASTIC FINITE ELEMENT MODEL UPDATING BASED ON POLYNOMIAL CHAOTIC EXPANSION AND KL DIVERGENCE

RESEARCH ON COOLING PERFORMANCE OF HIGH POWER POWER BATTERY PACK BASED ON MULTI-OBJECTIVE GENETIC ALGORITHM

Related Author

No data

Related Institution

College of Aerospace Science and Engineering, National University of Defense Technology

Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Mechanical Engineering,Hebei University of Technology

School of Mechanical Engineering, Hebei University of Technology

Hunan Sany Polytechnic College

School of Locomotive and Rolling Stock Engineering,Dalian Jiaotong University

⁰

暂无标题

DOI：10.16579/j.issn.1001.9669.2021.01.019

摘要

Abstract

关键词

Keywords

references