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Deep Learning-Based Average Consensus
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DOI：10.1109/ACCESS.2020.3014148.png)
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Abstract
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.
Keywords:
Machine learning
Signal processing algorithms
Acceleration
Heuristic algorithms
Tuning
Neural networks
Standards
Machine learning
multi-agent systems
networked control systems
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