Resistive-capacitive (RC) networks are used to model various processes in engineering, physics or biology. We consider the problem of recovering the network connection structure from measured input-output data. We address this problem as a structured identification one, that is, we assume to have a state-space model of the system (identified with standard techniques, such as subspace methods) and find a coordinate transformation that puts the identified system in a form that reveals the nodes connection structure. We characterize the solution set, that is, the set of all possible RC-networks that can be associated to the input-output data. We present a possible solution algorithm and show some computational experiments.

中文翻译：

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RC模型网络重构的结构化识别

阻容 (RC) 网络用于对工程、物理学或生物学中的各种过程进行建模。我们考虑从测量的输入输出数据中恢复网络连接结构的问题。我们将这个问题作为一个结构化的识别问题来解决，也就是说，我们假设有一个系统的状态空间模型（用标准技术识别，如子空间方法）并找到一个坐标变换，将识别的系统置于一种形式揭示节点连接结构。我们描述了解决方案集，即可以与输入-输出数据相关联的所有可能的 RC 网络的集合。我们提出了一种可能的求解算法并展示了一些计算实验。