Research on Threat Assessment evaluation model based on improved CNN algorithm

被引:3
作者
Feng, Yongjun [1 ]
Li, Mingxia [2 ]
Pei, Yongji [1 ]
Huang, Xinlei [3 ]
Wang, Hailong [3 ]
Li, Panpan [3 ]
机构
[1] State Grid Xinjiang Elect Power Corp, Urumqi 830063, Xinjiang, Peoples R China
[2] State Grid Xinjiang Elect Power Co Ltd, Res Inst, Urumqi 830011, Xinjiang, Peoples R China
[3] Mkt Serv Centerer State Grid Xinjiang Elect Power, Ltd Co Capital Intens Ctr & Measurement Ctr, Urumqi 830013, Xinjiang, Peoples R China
关键词
Improved CNN; Threat assessment; Multi-objective feature; Weight;
D O I
10.1007/s11042-023-16492-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In view of the traditional Threat Assessment (TA) evaluation model can only consider a single threat target, and the accuracy of threat evaluation is poor, the application effect of improved CNN algorithm in Ta evaluation model is studied. This paper proposes a TA evaluation model based on the improved Convolutional Neural Networks (CNN) algorithm. The model uses the powerful feature extraction ability of convolutional neural network, adopts the concept of dual channel neuron, improves the structure of convolutional neural network, and reduces the number of network parameters and obtains the target classification features with multiple markers on the basis of retaining the full connection layer. On this basis, fuzzy mathematics is used to quantitatively describe the classification features of multi marker targets, to define the weight value of each feature of targets, and to evaluate the threat degree of multiple targets by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The simulation results show that the model has fast convergence speed and accurate threat prediction ability, and can accurately obtain the threat ranking of multiple targets.
引用
收藏
页码:25351 / 25364
页数:14
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