Optimal Selection of Observations for Identification of Multiple Modules in Dynamic Networks
Optimal Selection of Observations for Identification of Multiple Modules in Dynamic Networks
复制标题
动态网络中多个模块识别观测值的优化选择
DOI：
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复制
发表时间：
2022
影响因子：
6.8
通讯作者：
D. Materassi
中科院分区：
文献类型：
--
作者：
Sina Jahandari;D. Materassi
本文提出了一个系统的算法来选择一组辅助测量，以一致地确定某些传递函数的动态网络从观测数据。通过最小化适当的成本函数来获得辅助测量的选择。假设网络的拓扑结构是已知的，强迫输入是不可测量的，并且观测值具有正的附加成本。本文证明了基于多输入单输出预测误差法的单个传递函数一致性辨识的充分必要条件等价于系统地处理网络的图形表示所得到的增广图中的最小割的概念。然后，可以使用不同的方法来找到使成本最小化的辅助测量的最佳集合，例如来自图论的算法（即，Ford-Fulkerson）、分布式算法（即，推-重新标记算法），或纯粹基于优化的过程（即，线性规划）。结果还扩展到更具挑战性的情况下，其中的目标是同时识别多个传递函数。
This article presents a systematic algorithm to select a set of auxiliary measurements in order to consistently identify certain transfer functions in a dynamic network from observational data. The selection of the auxiliary measurements is obtained by minimizing an appropriate cost function. It is assumed that the topology of the network is known, the forcing inputs are not measured, and the observations have positive additive costs. It is shown that sufficient and necessary conditions for consistent identification of a single transfer function based on a multi-input single-output prediction error method, are equivalent to the notion of minimum cut in an augmented graph resulted from systematically manipulating the graphical representation of the network. Then, the optimal set of auxiliary measurements minimizing the cost could be found using different approaches such as algorithms from graph theory (i.e., Ford-Fulkerson), distributed algorithms (i.e., push-relabel algorithm), or purely optimization based procedures (i.e., linear programming). The results are also extended to the more challenging scenario, where the objective is simultaneously identifying multiple transfer functions.
DOI：
10.1016/j.automatica.2011.03.008
发表时间：
2011-06
期刊：
Autom.
影响因子：
--
作者：
Ye Yuan;G. Stan;S. Warnick;J. Gonçalves
通讯作者：
Ye Yuan;G. Stan;S. Warnick;J. Gonçalves
影响因子：
6.8
作者：
Goncalves, Jorge;Warnick, Sean
通讯作者：
Warnick, Sean
DOI：
10.1073/pnas.95.25.14863
发表时间：
1998-12-08
影响因子：
11.1
作者：
Eisen, MB;Spellman, PT;Botstein, D
通讯作者：
Botstein, D