基于GRU网络的格兰杰因果网络重构  

Network Reconstruction via Granger Causality Based on GRU Network

作　　者：杨官学[1] 王家栋 YANG Guanxue;WANG Jiadong(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《软件导刊》2023年第10期49-57,共9页Software Guide

基　　金：国家自然科学基金项目(61903161)。

摘　　要：传统格兰杰因果依赖线性动力学,无法适应非线性应用场景的需求,因此提出一种基于GRU网络的格兰杰因果网络重构方法。该方法将整个网络重构划分为每个目标节点的邻居节点选择问题,针对每个目标节点构建基于GRU网络的格兰杰因果模型,在循环神经网络中引入简单的门控机制控制信息的更新方式,并对网络输入权重施加组稀疏惩罚以提取节点间的格兰杰因果关系。然后集成每一个子网络,获得最终完整的因果网络结构,并在GRU网络建模训练过程中考虑采用正则化的优化方法。通过线性矢量自回归、非线性矢量自回归、非均匀嵌入时滞矢量自回归、Lorenz-96模型及DREAM3竞赛数据集的实验表明,所提网络鲁棒性较强、有效性较高,在网络重构性能上具有明显的优越性。Reconstruction method of Granger causality network based on GRU network is proposed to address the traditional Granger causality that relies on linear dynamics and cannot meet the needs of nonlinear application scenarios.This method divides the entire network reconstruc⁃tion into neighbor node selection problems for each target node,constructs a Granger causality model based on GRU network for each target node,introduces a simple gating mechanism to control the update of information in the recurrent neural network,and applies a sparse penalty to the network input weight to extract the Granger causality between nodes.Then integrate each sub network to obtain the final complete causal network structure,and consider using regularization optimization methods during the GRU network modeling and training process.The experi⁃ments on linear vector autoregressive,nonlinear vector autoregressive,non-uniformly embedded time-delay vector autoregressive,Lorenz-96 model,and DREAM3 competition dataset show that the proposed network has strong robustness,high effectiveness,and obvious superiority in network reconstruction performance..

关 键 词：网络重构 因果推断 循环神经网络 格兰杰因果 门控循环单元 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]