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Identifying MIMO Hammerstein systems in the context of Subspace Model Identification Methods

机译:在子空间模型识别方法中识别MIMO Hammerstein系统

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In this paper, we outline the extension of the MOESP (standing for Multivariable Output Error State sPace model identification and introduced in [1].) family of subspace model identification schemes to Hammerstein type of non-linear systems. One type of identification problem is considered. This type addresses the identification of both the linear dynamic part and the static nonlinearity, where only limited a priori information regarding the structure of the nonlinearity is available. Another type of Hammerstein identification problem, considered in [2], assumes the (polynomial) structure of the static non-linearity to be given and the task here is to identify similarly the linear system dynamics and the unknown proportional constants in the parametrization of the static non-linearity. The improved robustness properties of the algorithms developed in this paper over existing correlation based schemes is illustrated in [2].
机译:在本文中,我们概述了MOESP(代表多变量输出错误状态sPace模型识别,并在[1]中引入)系列子空间模型识别方案对Hammerstein型非线性系统的扩展。考虑一种类型的识别问题。这种类型解决了线性动态部分和静态非线性的识别问题,其中只有有限的关于非线性结构的先验信息可用。 [2]中考虑的另一种类型的Hammerstein识别问题,假设要给出静态非线性的(多项式)结构,并且这里的任务是类似地识别线性系统动力学和未知参数比例常数。静态非线性。文献[2]中说明了在现有的基于相关性的方案上,本文所开发算法的改进的鲁棒性。

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