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Identification of a Benchmark Wiener-Hammerstein: A bilinear and Hammerstein-Bilinear model approach

机译:基准Wiener-Hammerstein的识别:双线性和Hammerstein-双线性模型方法

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In this paper the Wiener-Hammerstein Benchmark is identified as a bilinear discrete system. The bilinear approximation relies on both facts that the Wiener-Hammerstein system can be described by a Volterra series which can be approximated by bilinear systems. The identification is performed with an iterative bilinear subspace identification algorithm previously proposed by the authors. In order to increase accuracy, polynomial static nonlinearities were added to the bilinear model input. These Hammerstein type bilinear models are then identified using the same iterative subspace identification algorithm.
机译:本文将Wiener-Hammerstein基准确定为双线性离散系统。双线性逼近依赖于两个事实,即可以用Volterra级数描述Wiener-Hammerstein系统,而Volterra级数可以用双线性系统来近似。通过作者先前提出的迭代双线性子空间识别算法执行识别。为了提高精度,将多项式静态非线性添加到双线性模型输入中。然后使用相同的迭代子空间识别算法来识别这些Hammerstein型双线性模型。

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