Identification of continuous-time models for nonlinear dynamic systems from discrete data
Identification of continuous-time models for nonlinear dynamic systems from discrete data
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DOI：
10.1080/00207721.2015.1069906
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发表时间：
2016-09-09
影响因子：
4.3
通讯作者：
Wei, Hua-Liang
中科院分区：
文献类型：
--
作者：
Guo, Yuzhu;Guo, Ling Zhong;Wei, Hua-Liang
结合MF方法和iOFR算法，提出了一种新的iOFR-MF（迭代正交前向回归-调制函数）算法，用于从噪声数据中识别连续时间模型。在新方法中，首先构造一组候选项，描述系统状态之间或输入和输出之间的不同动态关系。然后使用 MF 方法对这些项进行调制以生成数据矩阵。接下来应用 iOFR 算法来构建这些调制项之间的关系，其中包括检测模型结构和估计相关参数。最终从调制项的模型中恢复原始变量之间的关系。非线性状态空间模型和一类高阶非线性输入输出模型都被考虑。将新的直接法与传统的有限差分法进行比较，结果表明新方法的性能明显优于有限差分法。即使测量受到噪声的严重干扰，新方法也能很好地发挥作用。还讨论了适当 MF 的选择。
A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input-output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed.