基于对抗自编码网络的水利数据补全方法

Water Conservancy Data Completion Method Based on Adversarial Autoencoders Network

在大规模监测系统中,监测点失效会导致数据缺失,从而影响数据分析结果的准确性。为此,提出一种对抗自编码的水利数据补全方法。利用自编码器构造生成网络,并提取监测点的数据特征,将其与训练好的判别网络进行对抗,最终补全待修复的监测数据。实验结果表明,与基于图正则化局部子表示方法相比,该方法具有较高的补全精确度,且均方误差较小,能够有效地重构监测数据。

In large-scale monitoring systems,failure of monitoring points can lead to data loss,which affects the accuracy of data analysis results.Therefoer,a method for complementing water conservancy data against self-encoding is proposed.The self-encoder is used to construct the network to extract the data features of the monitoring points,and then compete with the trained discriminant network to finally complete the monitoring data to be repaired.The experimental results show that compared with the graph-based regularized local sub-representation method,the proposed method has higher accuracy and less mean square error,which can effectively reconstruct the monitoring data.