论文标题
朝着自适应动态模式分解
Towards an Adaptive Dynamic Mode Decomposition
论文作者
论文摘要
动态模式分解（DMD）是一种基于数据的建模工具，该工具可以识别矩阵以在某个时候瞬间映射数量到将来的数量相同数量。我们设计了一个新版本，我们将其称为自适应动态模式分解（ADMD），该模式根据数据的性质利用时间延迟坐标，投影方法和过滤器，以创建可用问题的模型。过滤器在降低高维数据集的等级方面非常有效。我们将“离散的傅立叶变换”和“增强的拉格朗日乘数”纳入了我们的方法中的过滤器。提出的ADMD在几个不同复杂性的数据集上进行了测试，其性能似乎很有希望。
Dynamic Mode Decomposition (DMD) is a data based modeling tool that identifies a matrix to map a quantity at some time instant to the same quantity in future. We design a new version which we call Adaptive Dynamic Mode Decomposition (ADMD) that utilizes time delay coordinates, projection methods and filters as per the nature of the data to create a model for the available problem. Filters are very effective in reducing the rank of high-dimensional dataset. We have incorporated 'discrete Fourier transform' and 'augmented lagrangian multiplier' as filters in our method. The proposed ADMD is tested on several datasets of varying complexities and its performance appears to be promising.
