At present, Graph Neural Network (GNN) methods usually follow the node centered message passing process, and rely heavily on smooth node characteristics rather than graph structure. In view of this limitation, based on the heuristic method and graph attention mechanism, a feature fusion link prediction model (SAFL) combined with graph neural network is proposed. This model extracts the enclosing subgraphs around the target, combines the attention mechanism to assign neighbor weights to learn useful structural features, considers the impact of different nodes on the link, and fuses the graph neural network with the characteristics of input nodes to predict the link. The experiment on OGB dataset shows that the proposed link prediction model based on heuristic method enhances the graph structure characteristics, effectively represents the connectivity of enclosing subgraphs, and achieves better performance in link prediction.
Aiming at the problem that the existing air target tactical intention recognition methods cannot use the sequential characteristic and the multiple dimension of sensor data effectively, an air target tactical intention recognition method based on the fusion deep learning network model is proposed. According to the specific combat task, we select appropriate target features and intentions to construct the feature space and intention space of the target. Then we obtain the real-time target state data as time feature sequences. We assign weights to different features through attention mechanism, use convolutional neural network (CNN) to obtain local trend features of the time series and shorten the time series, use time convolutional network (TCN) to extract short-term local features in the data, and use bidirectional gating recurrent unit (BiGRU) to extract long-term dependence on data and considerate the future information to achieve combat intent recognition of air targets. Through contrast experiments with other models, the effectiveness and advantages of the proposed method are demonstrated.
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