Path-based multi-sources localization in multiplex networks

Cheng, Lan; Li, Xianghua; Han, Zhen; Luo, Tengyun; Ma, Lianbo; Zhu, Peican

doi:10.1016/j.chaos.2022.112139

Cited by 32 publications

(16 citation statements)

References 45 publications

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“…As shown in the simulation, the designed self-triggered algorithm is reasonable in terms of avoiding Zeno behavior. However, just quasisynchronization and quasi-anti-synchronization are considered, from the projective factor point of view, the time-varying factor is more universal for application, especially in social networks (Cheng et al, 2022), object/scene reconstruction (Tang et al, 2021), and 3D object recognition etc. Therefore, in future study, the obtained scheme will be developed for projective quasi-synchronization with time-varying projective factors and more practical networks will be considered.…”

Section: Discussionmentioning

confidence: 99%

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

Yuan

Luo

et al. 2022

Front. Neurorobot.

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The dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer control devices. This is partly because complete synchronization is difficult to realize under various projective factors and parameter mismatch. This article aims to investigate projective quasi-synchronization from the perspective of the controller. Here, two approaches are considered to find the event-triggered scheme for lag synchronization of coupled MNNs. In the first approach, the projective quasi-synchronization issue is formulated for coupled MNNs for the first time, where the networks are combined with time-varying delays and uncertainties under the constraints imposed by the frequency of controller updates within limited system communication resources. It is shown that our methods can avoid the Zeno-behavior under the newly determined triggered functions. In the second approach, following classical methods, a novel projective quasi-synchronization criterion that combines the nonlinear property of the memristor and the framework of Lyapunov-Krasovskii functional (LKF) is proposed. Simulation results indicate that the proposed two approaches are useful for coupled MNNs, and they have less control cost for different types of quasi-synchronization.

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Section: Discussionmentioning

confidence: 99%

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

Yuan

Luo

et al. 2022

Front. Neurorobot.

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“…The node contraction method [21] is a model to measure the importance of each node in a network. The process is to form a new network by absorbing the neighbors of a node into a new node and establishing connected edges with the neighbors of the neighboring nodes.…”

Section: Calculation Of Cohesiveness Of Aviation Network By Nodal Con...mentioning

confidence: 99%

An Optimization Method for Critical Node Identification in Aviation Network

Zhang

Zhao²,

Wang³

et al. 2022

Front. Phys.

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Air transport involves a long-distance journey; it is the main method of transnational travel, which is also an important connection channel between countries/regions. The aviation network is one of the core national transportation networks, whose importance is self-evident. To improve the management efficiency of the aviation network, this study studies the topological characteristics of the global aviation network in detail. The findings show that the global aviation network is a scale-free heterogeneous network, and the aviation network is tolerant of random faults. However, if key nodes are deliberately attacked, the network structure can easily be destroyed into fragments. To further explore the importance of nodes, combined with the background of airport mergers or unions, the node shrinkage method is improved by weighing network edges with the number of edges and ranking the importance of each node in the aviation network. This study compares the results of the node importance calculation by the node shrinkage method and improved weighted node shrinkage method, respectively. The results show that the ranking results obtained from the weighted node shrinkage method are better than those obtained from the traditional node shrinkage method. To further verify the validity of the weighted node shrinkage method, this study conducts a sensitivity analysis by calculating the weights of nodes and edges with different values. The results imply that the rank changes of node importance in the top 20 global aviation networks are the same. Therefore, it is important to find the key nodes in the aviation network and take corresponding protective measures to protect the stability of the global aviation network and improve the efficiency of the management of the aviation network.

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“…Individuals with more linkage position changes are considered to be more closely interacting. In the following evolutionary process, the more closely linked individuals can have a higher probability of reproduction (Cheng et al, 2022;Zhu et al, 2022).…”

Section: Decomposition Strategymentioning

confidence: 99%

Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

Chang²,

Yu³

2022

Front. Energy Res.

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The identification of decision variable interactions has a crucial role in the final outcome of the algorithm in the large-scale optimization domain. It is a prerequisite for decomposition-based algorithms to achieve grouping. In this paper, we design a recognition method with higher efficiency and grouping accuracy. It is based on the decomposition strategy of min hash to solve large-scale global optimization (LSGO) problems, called MHD. Our proposed method focuses on discovering the interactions of decision variables through min hash and forming subcomponents with a principle that the interdependencies between these subcomponents are maintained at a minimal level. This is described as follows: first, the min hash performs several permutations of the vector composed of decision variables. Second, the index value of the first non-zero row of the vector after rearrangement is found to obtain the new feature vector. Third, the probability of identical data at each position is calculated based on the new feature vector to decide whether there are some certain interactions between the decision variables. The advantages of min hash are: simpler computation and greater efficiency improvement than comparison between two or two decision variables; ability to find similar decision variables very quickly; and ability to cluster decision variables in a simple way. Therefore, the efficiency as well as the reliability of MHD is guaranteed. On the accuracy aspect, the proposed algorithm performs well in various types of the large-scale global optimization benchmark test function. Finally, the experimental results analysis and summarize the performance competitiveness of our proposed MHD algorithm from several aspects when it is used within a co-evolutionary framework.

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Path-based multi-sources localization in multiplex networks

Cited by 32 publications

References 45 publications

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

An Optimization Method for Critical Node Identification in Aviation Network

Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

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