.png){kind=icon}
返回
A continuous optimization framework for hybrid system identification
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2011.01.020.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function. (c) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Hybrid system
Identification
Robustness to outliers
Large data set
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:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
Identification of piecewise affine systems based on statistical clustering technique基于统计聚类技术的分段仿射系统辨识.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容