State-space LPV model identification using kernelized machine learning
State-space LPV model identification using kernelized machine learning
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DOI：
10.1016/j.automatica.2017.11.004
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发表时间：
2018-02-01
期刊：
影响因子：
6.4
通讯作者：
Meskin, Nader
中科院分区：
文献类型：
--
作者：
Rizvi, Syed Zeeshan;Velni, Javad Mohammadpour;Meskin, Nader
本文提出了一种状态空间形式的MIMO线性变参模型的非参数辨识方法。首先，通过在再现核希尔伯特空间（RKHS）中操作的非线性典型相关分析（CCA）将状态估计到相似变换。这使得重建过去和未来的输入、输出和调度变量之间的最小维度推理成为可能，从而可以估计与数据一致的状态序列。一旦状态被估计，一个基于最小二乘支持向量机（LS-SVM）的识别方案被制定，允许捕获估计状态空间模型的矩阵在调度变量上的依赖结构，而不需要显式声明这些通常未知的依赖关系；相反，它只需要选择非线性核函数和调优相关的超参数。(C) 2017 Elsevier Ltd.版权所有。
This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical correlation analysis (CCA) operating in a reproducing kernel Hilbert space (RKHS). This enables to reconstruct a minimal-dimensional inference between past and future input, output and scheduling variables, making it possible to estimate a state sequence consistent with the data. Once the states are estimated, a least-squares support vector machine (LS-SVM)-based identification scheme is formulated, allowing to capture the dependency structure of the matrices of the estimated state-space model on the scheduling variables without requiring an explicit declaration of these often unknown dependencies; instead, it only requires the selection of nonlinear kernel functions and the tuning of the associated hyper parameters. (C) 2017 Elsevier Ltd. All rights reserved.