A Local Direct Method for Module Identification in Dynamic Networks With Correlated Noise
A Local Direct Method for Module Identification in Dynamic Networks With Correlated Noise
复制标题
具有相关噪声的动态网络中模块识别的局部直接方法
DOI：
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发表时间：
2019
影响因子：
6.8
通讯作者：
P. V. D. Hof
中科院分区：
文献类型：
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作者：
K. R. Ramaswamy;P. V. D. Hof
最近，在假设不同节点上存在不相关的扰动的情况下，提出了获得模块动态一致估计的条件，从而解决了具有已知拓扑的动态网络中的局部模块的识别问题。这些条件通常反映在多输入单输出(MISO)识别设置中作为预测器输入的一组节点信号的选择。本文对不同节点信号上的过程噪声可以相互关联的情况进行了扩展，得到了一种辨识设置。在这种情况下，可能需要将本地模块嵌入多输入多输出(MIMO)识别设置中，以获得具有最大似然属性的一致估计。这需要妥善处理混杂的变量。其结果是一组算法，其基于给定的网络拓扑和干扰相关结构，选择适当的节点信号集合作为MISO或MIMO识别设置中的预测器输入和输出。提出了三种算法，它们在选择测量节点信号的方法上有所不同。可以考虑最大或最小数目的测量节点信号，以及预选的一组测量节点。
The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in an multiple-input-single-output (MISO) identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in an multiple-input--multiple-output (MIMO) identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is a set of algorithms that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in an MISO or MIMO identification setup. Three algorithms are presented that differ in their approach of selecting measured node signals. Either a maximum or a minimum number of measured node signals can be considered, as well as a preselected set of measured nodes.
影响因子：
6.8
作者：
Goncalves, Jorge;Warnick, Sean
通讯作者：
Warnick, Sean