论文标题
激发和测量的最佳分配，用于识别动态网络
Optimal allocation of excitation and measurement for identification of dynamic networks
论文作者
论文摘要
在本文中，正式陈述和分析了选择激发和测量的最佳分配和测量的问题。最佳选择是通过最低成本实验实现最准确的识别的选择。通过参数估计值的渐近协方差矩阵的痕迹评估精度，而成本标准是激发和测量的数量。在状态空间形式的两类动态网络中提供了分析和数值结果：分支和周期。从这些结果中，出现了许多选择的准则，这些准则基于网络拓扑或所识别模块的相对幅度。举例说明，这些准则在某种程度上可以应用于更通用拓扑的网络。
In this paper, the problem of choosing the best allocation of excitations and measurements for the identification of a dynamic network is formally stated and analyzed. The best choice will be one that achieves the most accurate identification with the least costly experiment. Accuracy is assessed by the trace of the asymptotic covariance matrix of the parameters estimates, whereas the cost criterion is the number of excitations and measurements. Analytical and numerical results are presented for two classes of dynamic networks in state space form: branches and cycles. From these results, a number of guidelines for the choice emerge, which are based either on the topology of the network or on the relative magnitude of the modules being identified. An example is given to illustrate that these guidelines can to some extent be applied to networks of more generic topology.
