查看更多>>摘要：? 2021 Elsevier LtdThis paper investigates the synchronization problem of complex-valued neural networks via event-triggered pinning impulsive control (ETPIC). A time-delayed pinning impulsive controller is proposed based on three levels of event-triggered conditions. By employing the Lyapunov functional method and differential inequality technique, sufficient delay-dependent synchronization criteria are derived under the proposed ETPIC scheme. The obtained result shows that synchronization of master and slave complex-valued neural networks can be achieved even if the sizes of delays exceed the length of intervals between any two consecutive impulsive instants determined by Lyapunov-based event-triggered conditions in the proposed control strategy. Moreover, the linear matrix inequality approach is utilized to exclude Zeno behavior. Numerical examples are provided to illustrate the effectiveness of the theoretical results.
查看更多>>摘要：? 2021 Elsevier LtdThe prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as virus spread analytics, social network analysis, earthquake location prediction, and typhoon tracking. The data describing these phenomena are multidimensional asynchronous event data that affect each other and show complex dynamic patterns in the continuous-time domain. Unlike the discrete characteristics formed by sampling at equal intervals of asynchronous time series, the timestamps of asynchronous events are in the continuous-time field. The study of these dynamic processes and the mining of their potential correlations provide a foundation for applying event propagation prediction, traceability, and causal inference at both the micro and macro levels. Most of the existing methods represent data as being embedded in the Euclidean space. However, when embedding a real-world graph with a tree-likeliness graph, Euclidean space cannot visually represent a graph. Inspired by the characteristics of hyperbolic space, we propose a model called Nostradamus to capture the propagation of the events of interest from historical events in a graph via the hyperbolic graph neural Hawkes process with Spatio-temporal characteristics. The Nostradamus’ core concept is to integrate the Hawkes process's conditional intensity function with a hyperbolic graph convolutional recurrent neural network.
NSTL
Elsevier