查看更多>>摘要：? 2021 Elsevier LtdThe networked systems are booming in multi-disciplines, including the industrial engineering system, the social system, and so on. The network structure is a prerequisite for the understanding and exploration of networked systems. However, the network structure is always unknown in practice, thus, it is significant yet challenging to investigate the inference of network structure. Although some model-based methods and data-driven methods, such as the phase-space based method and the compressive sensing based method, have investigated the structure inference tasks, they were time-consuming due to the greedy iterative optimization procedure, which makes them difficult to satisfy real-time structure inference requirements. Although the reconstruction time of L1 and other methods is short, the reconstruction accuracy is very low. Inspired by the powerful representation ability and time efficiency for the structure inference with the deep learning framework, a novel synergy method combines the deep residual network and fully connected layer network to solve the network structure inference task efficiently and accurately. This method perfectly solves the problems of long reconstruction time and low accuracy of traditional methods. Moreover, the proposed method can also fulfill the inference task of large scale complex network, which further indicates the scalability of the proposed method.
查看更多>>摘要：? 2021 Elsevier LtdEmotion classification based on neurophysiology signals has been a challenging issue in the literature. Recent neuroscience findings suggest that brain network structure underlying the different emotions provides a window in understanding human affection. In this paper, we propose a novel method to capture the distinct minimum spanning tree (MST) topology underpinning the different emotions. Specifically, we propose a hierarchical aggregation-based graph neural network to investigate the MST structure in emotion recognition. Extensive experiments on the public available DEAP dataset demonstrate the superior performance of the model in emotion classification as compared to existing methods. In addition, the results show that the theta, lower beta and gamma frequency band network information are more sensitive to emotions, suggesting a multi-frequency interaction in emotion processing.
查看更多>>摘要：? 2021 Elsevier LtdIn this article, we discuss bipartite fixed-time synchronization for fractional-order signed neural networks with discontinuous activation patterns. The Filippov multi-map is used to convert the fixed-time stability of the fractional-order general solution into the zero solution of the fractional-order differential inclusions. On the Caputo fractional-order derivative, Lyapunov-Krasovskii functional is proved to possess the indefinite fractional derivatives for fixed-time stability of fragmentary discontinuous systems. Furthermore, the fixed-time stability of the fractional-order discontinuous system is achieved as well as an estimate of the new settling time. The discontinuous controller is designed for the delayed fractional-order discontinuous signed neural networks with antagonistic interactions and new conditions for permanent fixed-time synchronization of these networks with antagonistic interactions are also provided, as well as the settling time for permanent fixed-time synchronization. Two numerical simulation results are presented to demonstrate the effectiveness of the main results
查看更多>>摘要：? 2021 Elsevier LtdDriven by industrial big data and intelligent manufacturing, deep learning approaches have flourished and yielded impressive achievements in the community of machine fault diagnosis. Nevertheless, current diagnosis models trained on a specific dataset seldom work well on other datasets due to the domain discrepancy. Recently, adversarial domain adaptation-based approaches have become an emerging and compelling tool to address this issue. Nonetheless, existing methods still have a shortcoming since they cannot guarantee sufficient feature similarity between the source domain and the target domain after adaptation, resulting in unguaranteed performance. To this end, a Cycle-consistent Adversarial Adaptation Network (CAAN) is advanced to realize more effective fault diagnosis of machinery. In CAAN, specifically, an adversarial game formed by the feature extractor and the domain discriminator is constructed to induce transferable feature learning. Meanwhile, the feature translators and discriminators between source and target domains are further designed to build a more comprehensive cycle-consistent generative adversarial constrain, which can more reliably ensure domain-invariant and class-separate characteristics of learned features. Extensive experiments constructed on three datasets from different mechanical systems demonstrate the effectiveness and superiority of CAAN.
查看更多>>摘要：? 2021 Elsevier LtdFinancial market predictions represent a complex problem. Most prediction systems work with the term time window, which is represented by exchange rate values of a real financial commodity. Such values (time window) provide the base for prediction of future values. Real situations, however, prove that prediction of only a single time-series trend is insufficient. This article aims at suggesting a novelty and unconventional approach based on the use of several neural networks predicting probable courses of a future trend defined in a prediction time window. The basis of the proposed approach is a suitable representation of the training-set input data into the neural networks. It uses selected FFT coefficients as well as robust output indicators based on a histogram of the predicted course of the selected currency pair. At the same time, the given currency pair enters the prediction in a combination with another three mutually interconnected currency pairs. A significant output of the articles is, apart from the proposed methodology, confirmation that the Elliott wave theory is beneficial in the trading environment and provides a substantial profit compared with conventional prediction techniques. That was proved in the performed experimental study.
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.
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