论文标题
RC模型网络重建的结构化识别
Structured identification for network reconstruction of RC-models
论文作者
论文摘要
电阻型（RC）网络用于建模工程，物理或生物学中的各种过程。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识别问题，也就是说，我们假设具有系统的状态空间模型（用标准技术识别，例如子空间方法），并找到一个坐标转换，将确定的系统以揭示节点连接结构的形式。我们表征解决方案集，即与输入输出数据相关联的所有可能的RC-NETWORK的集合。我们提出了一种可能的解决方案算法并显示了一些计算实验。
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.
