Identification of Auto-Regressive Exogenous Hammerstein Models Based on Support Vector Machine Regression
Identification of Auto-Regressive Exogenous Hammerstein Models Based on Support Vector Machine Regression
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DOI：
10.1109/tcst.2012.2228193
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发表时间：
2013-11
影响因子：
4.8
通讯作者：
M. Aldhaifallah;D. Westwick
中科院分区：
文献类型：
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作者：
M. Aldhaifallah;D. Westwick
本文将用于拟合标准支持向量机(SVMS)的算法推广到自回归外生(ARX)输入Hammerstein模型的辨识中，该模型由建模静态非线性的支持向量机和线性元素的ARX表示组成。模型参数可以通过最小化ε不敏感的损失函数来估计，该损失函数可以是线性的，也可以是二次的。此外，不确定性级别的值ε可以由用户指定，这使用户可以控制解的稀疏性。使用模拟数据和实验数据演示了这些选择的效果。
This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models consisting of a SVM, which models the static nonlinearity, followed by an ARX representation of the linear element. The model parameters can be estimated by minimizing an ε-insensitive loss function, which can be either linear or quadratic. In addition, the value of the uncertainty level, ε, can be specified by the user, which gives control over the sparseness of the solution. The effects of these choices are demonstrated using both simulated and experimental data.