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• W3173239121 abstract "Many complex, real-world dynamic systems need to be described using multilayer networks. Reconstruction of the multilayer nonlinear dynamic network structure from the observed streaming data is one of the core issues of dynamic system research. Although many methods have been proposed to reconstruct multilayer nonlinear dynamic networks from static data, they cannot reconstruct the multilayer network from streaming data. Because of the complexity of the problem, there is no nonlinear dynamic network reconstruction model and its optimization algorithm suitable for streaming data. To resolve this limitation, in this paper, a streaming data-driven multilayer dynamic network reconstruction framework called online multilayer network reconstruction (OMNR) is proposed. OMNR first establishes an online multilayer nonlinear dynamics network reconstruction model for streaming data and then proposes an online learning method based on follow-the-regularized-leader to optimize the reconstruction model. It considers only one case at a time to update the network structure; this is suitable for processing streaming data. Moreover, the space complexity of OMNR is very low and suitable for big-data tasks. Finally, using the Lorenz and Rossler dynamic systems of the multilayer network, it is verified that OMNR can solve the problem of multilayer dynamic network reconstruction driven by streaming data and fill the gap in the technology of multilayer nonlinear dynamic network reconstruction driven by streaming data." @default.
• W3173239121 created "2021-07-05" @default.
• W3173239121 creator A5035282947 @default.
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• W3173239121 date "2021-06-28" @default.
• W3173239121 modified "2023-09-27" @default.
• W3173239121 title "Multilayer nonlinear dynamical network reconstruction from streaming data" @default.
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• W3173239121 doi "https://doi.org/10.1360/sst-2020-0491" @default.
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