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Weighted Null-Space Fitting for Identification of Cascade Networks ?

机译：用于级联网络识别的加权零空间拟合 ？

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摘要

For identification of systems embedded in dynamic networks, the prediction error method (PEM) with a correct parametrization of the complete network provides asymptotically efficient estimates. However, the network complexity often hinders a successful application of PEM, which requires minimizing a non-convex cost function that can become more intricate for more complex networks. For this reason, identification in dynamic networks often focuses in obtaining consistent estimates of modules of interest. A downside of these approaches is that splitting the network in several modules for identification often costs asymptotic efficiency. In this paper, we consider dynamic networks with the modules connected in serial cascade, with measurements affected by sensor noise. We propose an algorithm that estimates all the modules in the network simultaneously without requiring the minimization of a non-convex cost function. This algorithm is an extension of Weighted Null-Space Fitting (WNSF), a weighted least-squares method that provides asymptotically efficient estimates for single-input single-output systems. We illustrate the performance of the algorithm with simulation studies, which suggest that a network WNSF method may also be asymptotically efficient when applied to cascade structures. Finally, we discuss the possibility of extension to more general networks affected by sensor noise.
机译：为了识别嵌入在动态网络中的系统，对整个网络进行正确参数化的预测误差方法（PEM）可提供渐近有效的估计。但是，网络复杂性通常会阻碍PEM的成功应用，这要求最小化非凸成本函数，而对于复杂网络而言，非凸成本函数可能变得更加复杂。由于这个原因，动态网络中的识别通常着重于获得感兴趣模块的一致估计。这些方法的缺点是将网络分为几个模块进行识别通常会花费渐近效率。在本文中，我们考虑将模块串联串联的动态网络，其测量结果受传感器噪声的影响。我们提出了一种算法，该算法可同时估算网络中的所有模块，而无需最小化非凸成本函数。该算法是加权零空间拟合（WNSF）的扩展，它是一种加权最小二乘方法，可为单输入单输出系统提供渐近有效的估计。我们通过仿真研究说明了该算法的性能，这表明将网络WNSF方法应用于级联结构时也可能渐近有效。最后，我们讨论了扩展到受传感器噪声影响的更通用网络的可能性。

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