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Sensitive Order Selection via Identification of Regularized FIR Models with Impulse Response Preservation

机译：通过识别具有冲激响应的正则化FIR模型来选择敏感订单

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摘要

The proposed modeling philosophy is calledimpulse response preserving (IRP)FIR modeling. It can be seen as a mixture of an OE and a FIR model combining the advantages and avoiding most drawbacks of both approaches. In the originally proposed approach via Gaussian processes a kernel needs to be established that expresses the prior knowledge about the process. The IRP approach is simpler, more straightforward, and much easier to understand, at least from a dynamic systems or controls point-of-view.The key prior information utilized by the IRP approach is the prior system ordern.Therefore the topic of order selection is also addressed. The IRP approach is significantly more robust w.r.t. the selected order. However, an extension of IRP that ensures plausible poles during optimization allows for a sensitive order selection.
机译：所提出的建模原理称为脉冲响​​应保持（IRP）FIR建模。可以将其视为OE和FIR模型的组合，结合了优点和避免了这两种方法的大多数缺点。在最初提出的通过高斯过程的方法中，需要建立一个内核来表达有关该过程的先验知识。至少从动态系统或控件的角度来看，IRP方法更简单，更直接且更容易理解.IRP方法使用的关键先验信息是先验系统定单。也得到解决。 IRP方法具有更强大的性能选定的订单。但是，IRP的扩展可确保优化过程中的合理极点，从而可以进行敏感的订单选择。

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