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- W3204265441 abstract "Identifying network topologies is a matter of great concern for us to better understand the evolutionary mechanisms and grasp the collective dynamics of complex networked systems. In this paper, a unified methodological framework for reconstructing nonlinear networks is proposed, termed Group Sparse Penalized Nonlinear Least Squares. Based on the theory of function approximation and feature selection, a nonlinear framework is firstly formulated with the equation of polynomial combination, where polynomial basis functions corresponding to the specific node should be taken as a group for either elimination or selection. Then the topology of complex networked system would be identified by solving the problem of sparse group optimization. Finally, the performance of our proposed method is evaluated on synthetic datasets from the classical Kuramoto oscillator model. Different influential cases of topology identification are also considered. All of the results demonstrate that the high-precision and robustness of our proposed method." @default.
- W3204265441 created "2021-10-11" @default.
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- W3204265441 date "2021-07-26" @default.
- W3204265441 modified "2023-09-24" @default.
- W3204265441 title "Topology Identification of Complex Networked System via Group Sparse Penalized Nonlinear Least Squares" @default.
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- W3204265441 doi "https://doi.org/10.23919/ccc52363.2021.9550104" @default.
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