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首页> 外文期刊>Automatic Control, IEEE Transactions on >Tensor Nuclear Norm LPV Subspace Identification
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Tensor Nuclear Norm LPV Subspace Identification

机译：张量核规范LPV子空间识别

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摘要

Linear parameter varying (LPV) subspace identification methods suffer from an exponential growth in number of parameters to estimate. This results in problems with ill-conditioning. In literature, attempts have been made to address the ill-conditioning by using regularization. Its effectiveness hinges on suitable a priori knowledge. In this paper, we propose using a novel, alternative regularization. That is, we first show that the LPV sub-Markov parameters can be organized into several tensors that are multilinear low rank by construction. Namely, their matricization along any mode is a low-rank matrix. Then, we propose a novel convex method with tensor nuclear norm regularization, which exploits this low-rank property. Simulation results show that the novel method can have higher performance than the regularized LPV-PBSIDopttechnique in terms of variance accounted for.
机译：线性参数变化（LPV）子空间识别方法要估计的参数数量呈指数增长。这导致不适的问题。在文献中，已经尝试通过使用正则化来解决不适。其有效性取决于适当的先验知识。在本文中，我们建议使用新颖的替代正则化。也就是说，我们首先表明，通过构造，LPV子马尔可夫参数可以组织为多个张量，这些张量为多线性低秩。即，它们沿任何模式的矩阵化都是低秩矩阵。然后，我们提出了一种具有张量核范数正则化的新凸方法，该方法利用了这种低秩性质。仿真结果表明，该新方法比规范化的LPV-PBSID n opt技术以方差为依据。

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