论文标题
线性动态网络中子网络的通用可识别性：完整的测量情况
Generic identifiability of subnetworks in a linear dynamic network: the full measurement case
论文作者
论文摘要
动态网络中单个或多个模块的可识别性条件指定在哪些条件下可以从测量信号的二阶统计属性中唯一恢复所考虑的模块。为了测量所有节点信号，并且网络的激发均通过测量的激发信号和未衡量的干扰输入提供了多个模块的通用性识别条件，即子网。此外，允许网络模型集包含固定的非参数化模块，例如反映用户已知动态的模块。条件以网络模型集的图表采取基于路径条件的形式。基于这些条件，合成结果是为分配外部激发信号以实现特定子网的一般可识别性而制定的。如果有足够数量的测量外部激发信号，则配制的结果会产生广义的间接识别算法类型，该算法仅需要测量网络中节点信号的子集。
Identifiability conditions for single or multiple modules in a dynamic network specify under which conditions the considered modules can be uniquely recovered from the second-order statistical properties of the measured signals. Conditions for generic identifiability of multiple modules, i.e. a subnetwork, are developed for the situation that all node signals are measured and excitation of the network is provided by both measured excitation signals and unmeasured disturbance inputs. Additionally, the network model set is allowed to contain non-parametrized modules that are fixed, and e.g. reflect modules of which the dynamics are known to the user. The conditions take the form of path-based conditions on the graph of the network model set. Based on these conditions, synthesis results are formulated for allocating external excitation signals to achieve generic identifiability of particular subnetworks. If there are a sufficient number of measured external excitation signals, the formulated results give rise to a generalized indirect type of identification algorithm that requires only the measurement of a subset of the node signals in the network.
