.png){kind=icon}
返回
Time-Delay Estimation Based on Graph Global Smoothness
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2023.3265743.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Time delay is an essential factor affecting the performance of time series-related tasks, such as time series forecasting, system modeling, and so on. Therefore, the time-delay estimation (TDE) issue has attracted more and more attention. In this article, a TDE method for the open-loop multi-in multi-out (MIMO) delay system with an unknown structure is proposed. In our approach, the theory of graph Laplacian is introduced to the TDE task for the first time. The correspondence between a delay system and a graph is established by constructing an N-linked graph from input-output samples of the delay system, and the global smoothness of the N-linked graph is considered as a time-delay metric. The TDE process of a delay system is then implemented by tracking the minimum of the global smoothness. The sparsity of the N-linked graph is further leveraged to effectively reduce the computational load of the TDE process. Simulation experiments and wind speed forecasting based on real data validate the effectiveness of the proposed method.
Keyword:
Delay systems
Delays
MIMO communication
Manifolds
Laplace equations
Sparse matrices
Estimation
Global smoothness
N-linked graph
system identification
time-delay estimation (TDE)
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏Identification of Discrete-Time Model With Integer Delay and Control Design for Cooling Processes With Application to Jacketed Crystallizers具有整数延迟的离散时间模型的识别和冷却过程的控制设计，并应用于夹套结晶器
PREAI
分享收藏A review of wind speed and wind power forecasting with deep neural networks基于深度神经网络的风速和风功率预测研究综述.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Population models involving the p-Laplacian with indefinite weight and constant yield harvesting
PREAI
分享收藏
查看更多内容