具有部分激励与测量的动态无环网络中的可辨识性
Identifiability in Dynamic Acyclic Networks with Partial Excitations and Measurements
AI总结：
针对仅部分顶点被激励和测量的线性时不变无环动态网络，提出保证全部模块可由测量数据一般可辨识的两个充分条件及执行器、传感器图形化配置方法。
摘要：
本文研究动态网络，其中顶点信号之间的因果关系由线性时不变传递函数（模块）表示。考虑一个无环网络，其中仅测量其顶点的一个子集，并激励顶点的一个子集，我们探究在测量数据基础上所有模块均可辨识的条件。文中给出了两个充分条件：第一个条件涉及需要对每个顶点执行的可辨识性分析；第二个条件基于树/反树覆盖概念，源于一种图形综合方法，用于在无环网络中分配执行器和传感器，以实现一般可辨识性。
英文摘要：
This paper deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considering an acyclic network where only a subset of its vertices are measured and a subset of the vertices are excited, we explore conditions under which all the modules are identifiable on the basis of measurement data. Two sufficient conditions are presented in the paper, where the first condition concerns an identifiability analysis that needs to be performed for each vertex, while the second condition, based on the concept of tree/anti-tree covering, results from a graphical synthesis approach to allocate actuators and sensors in an acyclic network for achieving generic identifiability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
