In this paper, the projective synchronization of quaternion-valued memristor-based neural networks with time-varing delays was studied. First, by utilizing set-valued map and differential inclusion theories, we reformulated the networks as an uncertain system with interval parameters. Then, through designing a novel controller and utilizing Lyapunov function and Young's inequality, several new synchronization conditions for projection synchronization of quaternion-valued memristor-based neural networks were obtained. Finally, the effectiveness of this method was demonstrated through a numerical example, underscoring its practical applicability.
Atmospheric motion vectors (AMVs), known as cloud track winds, have positive impacts on global numerical weather forecasts (NWP). In this study, AMVs that were retrieved from Fengyun-2G and Fengyun-4A were compared in their data quality and impacts on the typhoon forecasts in order to investigate the differences between the first and second generation of the geostationary meteorological satellites of China. This report conducted data evaluation and assimilation-forecasting experiments on FY-2G and FY-4A atmospheric motion vectors (AMVs), respectively. The results showed that the AMVs data of FY-4A are of better quality than those of FY-2G and assimilating the AMVs of FY-2G and FY-4A have a neutral to slightly positive impacts on typhoon forecasts, which is quite encouraging for their operational use in the future.
As a major research direction of temporal knowledge graph, the embedding and completion of temporal knowledge graph is a bridge for the application of temporal knowledge graph to various applications. Therefore, this research direction has a high position in the research of temporal knowledge graph.temporal knowledge graph embedding models are also receiving increasing attention. In this paper, we propose a modifier embedding model, and use this model to transform the quaternion of the temporal knowledge graph into a triplet. After that, we fused it with the DistMult model to obtain a new temporal knowledge graph embedding model, and conducted experiments on a dataset. Compared with existing models, our model has a good effect. After adjusting the embedding coefficients of the modified embedded model, we found that the embedding coefficients also have a certain impact on the effect of the model.
In this paper,Nevanlinna’s meromorphic function value distribution theory and normal family theory are used to study the theory of value distribution of meromorphic functions,which further extend the famous result due to Hayman,and enrich the value distribution theory of meromorphic functions.
为充分利用各个视角数据内在几何结构关系,提出一种新的基于自适应图正则非负矩阵分解的多视角聚类.该算法在一个统一的框架内,通过各视角亲和矩阵自适应学习提取共识的亲和矩阵进行图嵌入来提取多视角数据共识局部结构信息.另外,通过非负矩阵分解来提取多视角数据全局重构信息.最终使各个视角的共识表达,既保持了数据多视角共识全局重构信息,也保持了数据多视角局部结构信息.该优化问题在考虑了所有数据的一致性和每个视角之间互补性的同时,引入了各个视角数据的局部结构信息,达到数据表达和聚类的效果.通过4 组真实数据集的实验,结果表明所提出的方法与已有多视角聚类方法相比具有一定的优越性.
