Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic network case. In this paper, we consider the prediction error identification of the modules in a row of a dynamic network using the full input approach. Our main contribution is to propose a number of easily verifiable data informativity conditions for this identification problem. Among these conditions, we distinguish a sufficient data informativity condition that can be verified based on the topology of the network and a necessary and sufficient data informativity condition that can be verified via a rank condition on a matrix of coefficients that are related to a full-order model structure of the network. These data informativity conditions allow to determine different situations (i.e., different excitation patterns) leading to data informativity. In order to be able to distinguish between these different situations, we also propose an optimal experiment design problem that allows to determine the excitation pattern yielding a certain pre-specified accuracy with the least excitation power.

数据信息性是保证预测误差估计一致性的重要属性。因此，此属性已在开环和闭环情况下得到广泛研究，但在动态网络情况下仅被简要提及。在本文中，我们考虑使用全输入方法对动态网络的一行中的模块进行预测误差识别。我们的主要贡献是针对此识别问题提出了许多易于验证的数据信息性条件。在这些条件中，我们区分了可以根据网络拓扑验证的充分数据信息性条件和可以通过与全阶模型结构相关的系数矩阵上的秩条件来验证的必要且充分的数据信息性条件网络。这些数据信息性条件允许确定导致数据信息性的不同情况（即不同的激发模式）。为了能够区分这些不同的情况，我们还提出了一个优化实验设计问题，该问题允许确定以最小激发功率产生一定预定精度的激发模式。