Inferring time-delayed dynamic networks with nonlinearity and nonuniform lags
Inferring time-delayed dynamic networks with nonlinearity and nonuniform lags
复制标题
推断具有非线性和非均匀滞后的时滞动态网络
DOI：
10.1209/0295-5075/119/28001
复制
发表时间：
2017-07
期刊：
影响因子：
1.8
通讯作者：
Wang Xiaofan
中科院分区：
文献类型：
--
作者：
Yang Guanxue;Wang Lin;Wang Xiaofan
基于时间序列数据的非线性网络系统节点之间重建时间延迟的交互是很重要且具有挑战性的，尤其是对于仅噪声数据有限但对节点动力学不了解的情况。在本文中，通过融合
Reconstructing time-delayed interactions among nodes of nonlinear networked systems based on time-series data is important and challenging, especially for the cases with only limited noisy data but no knowledge of node dynamics. In this paper, by fusing multiple source datasets together, we propose a data-driven modeling method based on noisy time series, referred to as nonuniform embedding nonlinear conditional Granger causality (NENCGC), specially focusing on the nonlinearity and nonuniform time-delayed characteristics of real networked systems. Specifically, we first use a nonuniform embedding scheme to select causal lagged components and then group these selected lagged components into different clusters of different nodes. In nonlinear causal analysis, the lagged components in the same cluster are treated as a whole through radial basis functions to fit the nonlinear relationships among nodes. Compared with other popular methods, our proposed NENCGC is proved effective and accurate in discovering time-delayed interactions from noisy data in terms of standard metrics. Meanwhile, both superiority and robustness of NENCGC against the variations of samples, time delays, noise intensities, as well as coupling strengths, are demonstrated.
DOI：
10.1073/pnas.092576199
发表时间：
2002-04-30
影响因子：
11.1
作者：
Yeung, MKS;Tegnér, J;Collins, JJ
通讯作者：
Collins, JJ
影响因子：
--
作者：
Fujita A;Sato JR;Garay-Malpartida HM;Yamaguchi R;Miyano S;Sogayar MC;Ferreira CE
通讯作者：
Ferreira CE