摘要
针对供应链数据设计一种基于改进GAN(Generative Adversarial Network)的异常识别模型。运用联合分布和多配对样本Friedman检验对供应链数据进行探索性分析。针对数据特性进行异常检测,为了捕捉数据的时间相关性,利用LSTM(Long Short-Term Memory)作为生成器和判别器的基础模型,并在生成器中用Cycle Consistency损失防止编码器和解码器矛盾,判别器中用Wasserstein损失克服模式崩溃问题,同时引入非参数动态阈值方法进行优化,进而识别异常。运用精确率、召回率、F1值进行模型评价,并与基线方法进行比较研究。结果表明,该改进模型更贴近供应链数据的实际情况,可增强供应链柔性,具有较高的异常识别性能。
An improved GAN-based anomaly identification model is designed for supply chain data.An exploratory analysis of the supply chain data was carried out using joint distribution and multi-paired sample Friedman test.The anomaly detection was performed with the data characteristics.To capture the temporal correlation of the data,LSTM was used as the base model for the generator and discriminator,and Cycle Consistency loss was used in the generator to prevent encoder and decoder conflicts,and Wasserstein loss was used in the discriminator to overcome the pattern collapse problem,while a non-parametric dynamic thresholding method was introduced for optimization,and thus identifying anomalies.The model was evaluated using accuracy,recall and F1 values and studied in comparison with the baseline method.The results show that the improved model is closer to the actual situation of supply chain data,can enhance supply chain flexibility and has high anomaly identification performance.
作者
邹昕彤
金辉
Zou Xintong;Jin Hui(School of Automobile and Traffic Engineering,Liaoning University of Technology,Jinzhou 121000,Liaoning,China)
出处
《计算机应用与软件》
北大核心
2025年第7期107-110,174,共5页
Computer Applications and Software
