This paper aims to investigate the stochastic resonance （SR） in an FitzHugh-Nagumo （FHN） model with an additive LEvy noise numerically. The non-Gaussian LEvy noise is a kind of general random noise which is differ...This paper aims to investigate the stochastic resonance （SR） in an FitzHugh-Nagumo （FHN） model with an additive LEvy noise numerically. The non-Gaussian LEvy noise is a kind of general random noise which is different from the usual Gaussian noise, and it has small fluctuations with the unpredictable jumps to describe the random fluctuations in an FHN model. SR is determined by the signal-to-noise ratio （SNR）, and the numerical simulation results show the occurrence of the SR phenomena in the given FHN system. The influence of various parameters of the LEvy noise and the FHN model on the SR will be exam- ined, and some mechanisms of the LEvy noise-induced SR are presented which are different from those of the Gaussian noise.展开更多
The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing conne...The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.展开更多
基金supported by the the National Natural Science Foundation of China(Grant Nos.11372247&11472224)the NPU Foundation for Undergraduate Graduation Design
文摘This paper aims to investigate the stochastic resonance （SR） in an FitzHugh-Nagumo （FHN） model with an additive LEvy noise numerically. The non-Gaussian LEvy noise is a kind of general random noise which is different from the usual Gaussian noise, and it has small fluctuations with the unpredictable jumps to describe the random fluctuations in an FHN model. SR is determined by the signal-to-noise ratio （SNR）, and the numerical simulation results show the occurrence of the SR phenomena in the given FHN system. The influence of various parameters of the LEvy noise and the FHN model on the SR will be exam- ined, and some mechanisms of the LEvy noise-induced SR are presented which are different from those of the Gaussian noise.
文摘The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.
