电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先...电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先,该方法从多个维度选取电商财务风险相关指标,以全面反映电商企业的财务健康状况。随后,对选取的指标数据进行预处理,确保数据质量和模型训练的准确性。接下来,建立一个改进的BP神经网络模型,用于电商财务风险的预测。在模型建立过程中,特别关注学习速率的调整,通过改变学习率来平衡模型的训练速度和稳定性,从而实现财务风险预测。实验结果表明:基于改进BP神经网络的电商财务风险智能预测方法实现了每2 min更新一次的高频率,其平均更新时间仅为1 s左右,更新成功率稳定在99%以上,在更新能力方面表现优秀,可为电商企业的财务风险预测提供新的解决方案。The financial data of e-commerce enterprises usually involve a large number of transactions and complex business logic, and the collection, cleaning and sorting of data require a lot of time and manpower, resulting in a low frequency of data update, which limits the accuracy of financial risk prediction model. Therefore, this paper studies the intelligent prediction method of financial risk of e-commerce based on improved BP neural network. First, the method selects indicators related to e-commerce financial risks from multiple dimensions to comprehensively reflect the financial health of e-commerce enterprises. Then, the selected index data is preprocessed to ensure the data quality and the accuracy of model training. Next, an improved BP neural network model is established to predict the financial risk of e-commerce. In the process of model building, we pay special attention to the adjustment of learning rate, and balance the training speed and stability of the model by changing the learning rate, so as to realize the financial risk prediction. The experimental results show that the e-commerce financial risk intelligent prediction method based on improved BP neural network can achieve a high frequency of update every 2 min, the average update time is only about 1 s, and the success rate of update is stable at more than 99%, which has excellent performance in updating ability, and can provide a new solution for the financial risk prediction of e-commerce enterprises.展开更多
通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数...通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数据。计算每个节点的重要性,利用节点的重要性和时间序列数据,通过特定的算法,获取每个节点的预测值,从而实现无特征网络链路的预测。实验结果表明,该方法预测评估结果的AUC值较高,能够更准确地预测网络中的链路连接。展开更多
文摘电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先,该方法从多个维度选取电商财务风险相关指标,以全面反映电商企业的财务健康状况。随后,对选取的指标数据进行预处理,确保数据质量和模型训练的准确性。接下来,建立一个改进的BP神经网络模型,用于电商财务风险的预测。在模型建立过程中,特别关注学习速率的调整,通过改变学习率来平衡模型的训练速度和稳定性,从而实现财务风险预测。实验结果表明:基于改进BP神经网络的电商财务风险智能预测方法实现了每2 min更新一次的高频率,其平均更新时间仅为1 s左右,更新成功率稳定在99%以上,在更新能力方面表现优秀,可为电商企业的财务风险预测提供新的解决方案。The financial data of e-commerce enterprises usually involve a large number of transactions and complex business logic, and the collection, cleaning and sorting of data require a lot of time and manpower, resulting in a low frequency of data update, which limits the accuracy of financial risk prediction model. Therefore, this paper studies the intelligent prediction method of financial risk of e-commerce based on improved BP neural network. First, the method selects indicators related to e-commerce financial risks from multiple dimensions to comprehensively reflect the financial health of e-commerce enterprises. Then, the selected index data is preprocessed to ensure the data quality and the accuracy of model training. Next, an improved BP neural network model is established to predict the financial risk of e-commerce. In the process of model building, we pay special attention to the adjustment of learning rate, and balance the training speed and stability of the model by changing the learning rate, so as to realize the financial risk prediction. The experimental results show that the e-commerce financial risk intelligent prediction method based on improved BP neural network can achieve a high frequency of update every 2 min, the average update time is only about 1 s, and the success rate of update is stable at more than 99%, which has excellent performance in updating ability, and can provide a new solution for the financial risk prediction of e-commerce enterprises.
文摘通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数据。计算每个节点的重要性,利用节点的重要性和时间序列数据,通过特定的算法,获取每个节点的预测值,从而实现无特征网络链路的预测。实验结果表明,该方法预测评估结果的AUC值较高,能够更准确地预测网络中的链路连接。
