Deep Learning-Based Average Consensus
Deep Learning-Based Average Consensus
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DOI：
10.1109/access.2020.3014148
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Wadayama, Tadashi
中科院分区：
文献类型：
--
作者：
Kishida, Masako;Ogura, Masaki;Wadayama, Tadashi
在本研究中，我们分析了复杂网络中线性平均共识算法的加速问题。我们提出了一种数据驱动的方法，使用深度学习技术来调整时间（即时变）网络的权重。该方法在给定有限时间窗口的情况下，首先展开线性平均共识协议，得到一个前馈信号流图，将其视为一个神经网络。然后使用标准深度学习技术训练获得的神经网络的边权，以在给定的有限时间窗口内最小化共识误差。通过这个训练过程，我们得到了一组优化的时变权值，对于一个复杂的网络可以更快地达成共识。我们还证明了所提出的方法可以推广到无限时间窗问题。数值实验表明，与基线策略相比，我们的方法可以实现更小的共识误差。
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.