.png){kind=icon}
返回
System identification of simplified crash models using multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cma.2005.09.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A multi-objective optimization (MOO) based methodology is presented to identify simplified dynamic system simulation models. The proposed methodology is used to develop a three degree-of-freedom model for an automotive crash simulation. To date, such system identification problems have only been approached as single-objective problems. We use various MOO methods to provide new insight into the problem. Furthermore, we use this problem to study the nature of the Pareto optimal hypersurface, normalization of objectives, and specification of preferences. In general, we find that the MOO-based methodology is quite useful for dynamic system identification problems. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
optimization
multi-objective
crash models
dynamic system identification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
机构
暂无机构信息
引用论文
.png){kind=icon}
没有更多内容
没有更多内容