Robust dynamical network structure reconstruction
Robust dynamical network structure reconstruction
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DOI：
10.1016/j.automatica.2011.03.008
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Ye Yuan;G. Stan;S. Warnick;J. Gonçalves
中科院分区：
文献类型：
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作者：
Ye Yuan;G. Stan;S. Warnick;J. Gonçalves
本文解决了基于数据的网络重构问题。以前的工作确定了LTI系统网络重构的必要和充分条件，假设完美的测量（无噪声）和完美的系统识别。本文假设网络重构的条件已经满足，但这里我们额外考虑了噪声和未建模的动力学（包括非线性）。为了识别产生数据的网络结构，我们计算测量数据和由特定网络结构产生的数据之间的最小距离。最后，我们以包括噪声和非线性在内的生物启发的网络重建示例作为结论。
This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities.