基于样本均衡与特征交互的通信网络故障预测方法

来源 :北京邮电大学学报 | 被引量 : 0次 | 上传用户：xuguai19811025

【摘要】

：

通信网络故障预测数据集样本不均衡,影响故障预测的准确性,对此,提出了基于样本均衡与特征交互的通信网络故障预测方法.首先,将基于Wasserstein距离的梯度惩罚生成对抗网络(WGAN-GP)用于生成新的少数类样本,解决了告警数据集中存在的样本不均衡问题,并提出了嵌入记忆向量的特征生成卷积神经网络(M-FGCNN)模型.利用多层感知器和卷积神经网络加强特征间的交互,将告警领域专家经验与因子分解机模型结合生成新的告警特征;在模型的嵌入矩阵中加入记忆向量并改进了模型的损失函数,增强了模型的记忆性.在样本不均衡

【作者】

：

贾珺冯春燕夏海轮张天魁李成钢

【机构】

：

北京邮电大学信息与通信工程学院,北京100876;中国电信集团系统集成有限责任公司, 100035

【出处】

：

北京邮电大学学报

【发表日期】

：

2021年6期

【关键词】

：

故障预测深度学习生成对抗网络

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基于样本均衡与特征交互的通信网络故障预测方法

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