.png){kind=icon}
返回
Deep Learning-Based Average Consensus
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3014148.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this study, we analyzed the problem of accelerating the linear average consensus algorithm for complex networks. We propose a data-driven approach to tuning the weights of temporal (i.e., time-varying) networks using deep learning techniques. Given a finite-time window, the proposed approach first unfolds the linear average consensus protocol to obtain a feedforward signal-flow graph, which is regarded as a neural network. The edge weights of the obtained neural network are then trained using standard deep learning techniques to minimize consensus error over a given finite-time window. Through this training process, we obtain a set of optimized time-varying weights, which yield faster consensus for a complex network. We also demonstrate that the proposed approach can be extended for infinite-time window problems. Numerical experiments revealed that our approach can achieve a significantly smaller consensus error compared to baseline strategies.
Keyword:
Machine learning
Signal processing algorithms
Acceleration
Heuristic algorithms
Tuning
Neural networks
Standards
Machine learning
multi-agent systems
networked control systems
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:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Plumbagin induces ROS-mediated apoptosis in human promyelocytic leukemia cells in vivoPlumbagin体内诱导ROS介导的人早幼粒细胞白血病细胞凋亡
.png){kind=icon}
PREAI
分享收藏分享收藏A multi-task neural network for multilingual sentiment classification and language detection on Twitter用于Twitter多语言情感分类和语言检测的多任务神经网络
PREAI
分享收藏
查看更多内容