论文标题
复杂值时空图形卷积神经网络及其在电力系统AI中的应用
Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
论文作者
论文摘要
大型结构化数据上图的有效表示，进攻，分析和可视化引起了很多关注。到目前为止，大多数文献都集中在实现的信号上。但是，信号通常在傅立叶域中稀疏，并且可以使用其光谱组件的复杂信封来获得更多信息和紧凑的表示形式，而不是原始的现实价值信号。在这一事实中，在这项工作中，我们将图形卷积神经网络（GCN）推广到复杂域，从而得出了允许在图形过滤器（GF）的定义和过程复杂价值价值的图形信号（GS）的定义中纳入复杂值的图形移动器（GSO）的理论。开发的理论可以处理时空复杂的网络过程。我们证明，相对于基础图支持的扰动，传输误差的界限以及通过乘数层传播的界限，复合物值GCN是稳定的。然后，我们将复杂的GCN应用于电网状态预测，电网网络攻击检测和定位。
The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, signals are often sparse in the Fourier domain, and more informative and compact representations for them can be obtained using the complex envelope of their spectral components, as opposed to the original real-valued signals. Motivated by this fact, in this work we generalize graph convolutional neural networks (GCN) to the complex domain, deriving the theory that allows to incorporate a complex-valued graph shift operators (GSO) in the definition of graph filters (GF) and process complex-valued graph signals (GS). The theory developed can handle spatio-temporal complex network processes. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support, the bound of the transfer error and the bound of error propagation through multiply layers. Then we apply complex GCN to power grid state forecasting, power grid cyber-attack detection and localization.
