Dynamic_Mode_Decomposition
所属分类：其他
开发工具：matlab
文件大小：13806KB
下载次数：2
上传日期：2021-04-13 14:57:28
上 传 者：
西门吹雪
说明： 一个非常有用、且实现起来简单直接的数据分析方法，叫做动态模态分解（其英文名是：dynamic mode decomposition），它被用于发现潜藏着的“动力系统”，尤其是“时空上的拟序结构（spatial-temporal coherent structure）。这个方法起初是被做“流体力学”的人所提出，后来又被应用于与动力系统相关的科学、工程学当中。
(A very useful data analysis method that is simple and straightforward to implement is called dynamic mode decomposition, which is used to discover the hidden "dynamic system", especially the "simulation in time and space". Spatial-temporal coherent structure. This method was first proposed by people who do "fluid mechanics", and later it was applied to science and engineering related to power systems.)
文件列表:
Dynamic Mode Decomposition (0, 2021-04-13)
Dynamic Mode Decomposition\DMD bayesian (0, 2020-11-10)
Dynamic Mode Decomposition\DMD bayesian\0392.pdf (372150, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\bdmd.m (8618, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\bplot.m (13875, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\cnormrnd.m (411, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\defaultopt.m (1465, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\demo.m (1188, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\gigrnd.m (2844, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\median_complex.m (268, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\sdmd.m (700, 2020-09-07)
Dynamic Mode Decomposition\DMD bayesian\sortsample.m (1428, 2020-09-07)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\2019 Dynamic mode decomposition of rs and task fMRI NeuroImage.pdf (2548464, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER\DMD_eig.m (934, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER\FFTDMD_spectrum.m (1486, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER\LDS_DMD_eig.m (3563, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER\optimal_SVHT_coef.m (4968, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH08_NOISEPOWER\SVHT_cylinder.m (1069, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\Algorithm_9_1.m (415, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\compressedDMD.m (1961, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\computeDMDModes.m (1029, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\computeFFTModes.m (455, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\computePODModes.m (304, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\getParms.m (419, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\getSparseData.m (631, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\plotData.m (1489, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\projectData.m (837, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX1_TORUS\runExample.m (625, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX2_CYLINDER (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX2_CYLINDER\CCcool.mat (291, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX2_CYLINDER\cDMD_p40_r21_VORT.m (1923, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX2_CYLINDER\csDMD_p1000_r21_VORT.m (2068, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\EX2_CYLINDER\plotCylinderNoSave.m (1313, 2020-09-06)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\utils (0, 2020-11-10)
Dynamic Mode Decomposition\DMD data-driven modeling of complex systems\CH09_SPARSITY\utils\coolcolor.mat (620, 2020-09-06)
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# Bayesian Dynamic Mode Decomposition
Implementation of Bayesian dynamic mode decomposition (Bayesian DMD) by authors of the IJCAI paper (see below).
## Prerequisites
- MATLAB 2015a or later
- Statistics toolbox
## Demo Script
Run `demo.m` for a simple demonstration of Bayesian DMD.
## Author
* **Naoya Takeishi** - [http://www.naoyatakeishi.com/](http://www.naoyatakeishi.com/)
## Reference
Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, and Takehisa Yairi, "Bayesian Dynamic Mode Decomposition," in *Proc. of the 26th Int'l Joint Conf. on Artificial Intelligence (IJCAI)*, pp. 2814-2821, 2017.
## License
This project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details
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