With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing al...With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value.展开更多
现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结...现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结合复杂网络微观结构和宏观结构构造节点特征;其次,针对特征冗余问题,提出一个融合选择性状态空间模型(State Space Models)和自监督学习的节点特征提取方法;最后,针对泛化性低问题,利用图结构学习在模型训练层面优化损失函数提高分类精度.利用4个公开数据集上进行了广泛实验,本文方法优于次优方法4.66%,节点分辨率保持稳定.实验表明,所提出方法能有效的识别不同网络的关键节点.展开更多
工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出...工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出了通信图建模方法。该方法设计了多层图卷积结构与注意力机制,构建了多类别故障标签并实现动态图序列预测。实验结果表明,该方法在预测准确率、故障识别能力与推理效率方面优于传统模型,可以有效感知节点间拓扑与状态演化关系,在工业通信网络中具备部署价值与应用前景。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.52074126)Tangshan Science and Technology Plan Project(Grant No.22130201G).
摘要With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value.
摘要现有复杂网络关键节点识别方法中缺少对节点本身特征的研究,存在网络拓扑信息提取不全面、特征冗余、泛化性低等问题.为了解决上述问题,本文提出一种基于图结构学习的复杂网络关键节点识别方法.首先,针对网络拓扑信息提取不全面问题,结合复杂网络微观结构和宏观结构构造节点特征;其次,针对特征冗余问题,提出一个融合选择性状态空间模型(State Space Models)和自监督学习的节点特征提取方法;最后,针对泛化性低问题,利用图结构学习在模型训练层面优化损失函数提高分类精度.利用4个公开数据集上进行了广泛实验,本文方法优于次优方法4.66%,节点分辨率保持稳定.实验表明,所提出方法能有效的识别不同网络的关键节点.
摘要工业物联网(Industrial Internet of Things,IIoT)中通信故障具有突发性与结构依赖性,提升节点异常状态的预测精度对保障IIoT系统的稳定运行具有重要意义。文章构建了基于图神经网络(Graph Neural Network,GNN)的通信故障预测模型,提出了通信图建模方法。该方法设计了多层图卷积结构与注意力机制,构建了多类别故障标签并实现动态图序列预测。实验结果表明,该方法在预测准确率、故障识别能力与推理效率方面优于传统模型,可以有效感知节点间拓扑与状态演化关系,在工业通信网络中具备部署价值与应用前景。