A continuous optimization framework for hybrid system identification
A continuous optimization framework for hybrid system identification
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DOI：
10.1016/j.automatica.2011.01.020
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发表时间：
2011-03-01
期刊：
影响因子：
6.4
通讯作者：
Vidal, Rene
中科院分区：
文献类型：
--
作者：
Lauer, Fabien;Bloch, Gerard;Vidal, Rene
我们提出了一种新的混合系统识别框架，该框架依赖于持续优化。该框架基于成本函数的最小化，可以选择损失函数的最小值或乘积。前者受到传统估计方法的启发，而后者则受到最近用于混合系统识别的代数和支持向量回归方法的启发。在这两种情况下，识别问题都被改写为仅涉及模型的实际参数作为变量的连续优化程序，从而避免了离散优化的使用。即使对于非常大的数据集，也可以通过使用标准优化方法有效地解决该程序。此外，所提出的框架通过损失函数的选择轻松地结合了对不同类型异常值的鲁棒性。 (c) 2011 Elsevier Ltd. 保留所有权利。
We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function. (c) 2011 Elsevier Ltd. All rights reserved.