ON GLOBAL IDENTIFIABILITY FOR ARBITRARY MODEL PARAMETRIZATIONS
ON GLOBAL IDENTIFIABILITY FOR ARBITRARY MODEL PARAMETRIZATIONS
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DOI：
10.1016/0005-1098(94)90029-9
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发表时间：
1994-02-01
期刊：
影响因子：
6.4
通讯作者：
GLAD, T
中科院分区：
文献类型：
--
作者：
LJUNG, L;GLAD, T
识别的一个基本问题是，甚至在数据分析之前就能够确定模型结构的所有自由参数是否可以从数据中唯一地恢复。这就是全球可识别性的问题。在这篇文章中，我们展示了如何使用微分代数的概念和算法来分析任意模型结构（基本上具有解析非线性）的全局可识别性。它展示了如何将全局结构可识别性问题简化为给定模型结构是否可以重新排列为线性回归的问题。还给出了一个显式的算法来测试这一点。此外，输入的“持续激励”问题也可以通过类似的方式进行明确测试。所涉及的算法非常适合在计算机代数中实现。还描述了一种这样的实现。
It is a fundamental problem of identification to be able-even before the data have been analyzed-to decide if all the free parameters of a model structure can be uniquely recovered from data. This is the issue of global identifiability. In this contribution we show how global identifiability for an arbitrary model structure (basically with analytic non-linearities) can be analyzed using concepts and algorithms from differential algebra. It is shown how the question of global structural identifiability is reduced to the question of whether the given model structure can be rearranged as a linear regression. An explicit algorithm to test this is also given. Furthermore, the question of 'persistent excitation' for the input can also be tested explicitly is a similar fashion. The algorithms involved are very well suited for implementation in computer algebra. One such implementation is also described.