Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising.
A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm fo...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6383872?pdf=render |
| _version_ | 1819206103472275456 |
|---|---|
| author | Taku Nonomura Hisaichi Shibata Ryoji Takaki |
| author_facet | Taku Nonomura Hisaichi Shibata Ryoji Takaki |
| author_sort | Taku Nonomura |
| collection | DOAJ |
| description | A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm for dataset for a small number of degree of freedom (DoF). It also illustrates that EKFDMD requires significant numerical resources for many-degree-of-freedom (many-DoF) problems and that the combination with truncated proper orthogonal decomposition (trPOD) helps us to apply the EKFDMD algorithm to many-DoF problems, though it prevents the algorithm from being fully online. The numerical experiments of a noisy dataset with a small number of DoFs illustrate that EKFDMD can estimate eigenvalues better than or as well as the existing algorithms, whereas EKFDMD can also denoise the original dataset online. In particular, EKFDMD performs better than existing algorithms for the case in which system noise is present. The EKFDMD with trPOD, which unfortunately is not fully online, can be successfully applied to many-DoF problems, including a fluid-problem example, and the results reveal the superior performance of system identification and denoising. |
| first_indexed | 2024-12-23T05:02:17Z |
| format | Article |
| id | doaj.art-461d227f51b14bbe83b9af1b9950d3d8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T05:02:17Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-461d227f51b14bbe83b9af1b9950d3d82022-12-21T17:59:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01142e020983610.1371/journal.pone.0209836Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising.Taku NonomuraHisaichi ShibataRyoji TakakiA new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm for dataset for a small number of degree of freedom (DoF). It also illustrates that EKFDMD requires significant numerical resources for many-degree-of-freedom (many-DoF) problems and that the combination with truncated proper orthogonal decomposition (trPOD) helps us to apply the EKFDMD algorithm to many-DoF problems, though it prevents the algorithm from being fully online. The numerical experiments of a noisy dataset with a small number of DoFs illustrate that EKFDMD can estimate eigenvalues better than or as well as the existing algorithms, whereas EKFDMD can also denoise the original dataset online. In particular, EKFDMD performs better than existing algorithms for the case in which system noise is present. The EKFDMD with trPOD, which unfortunately is not fully online, can be successfully applied to many-DoF problems, including a fluid-problem example, and the results reveal the superior performance of system identification and denoising.http://europepmc.org/articles/PMC6383872?pdf=render |
| spellingShingle | Taku Nonomura Hisaichi Shibata Ryoji Takaki Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. PLoS ONE |
| title | Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. |
| title_full | Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. |
| title_fullStr | Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. |
| title_full_unstemmed | Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. |
| title_short | Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising. |
| title_sort | extended kalman filter based dynamic mode decomposition for simultaneous system identification and denoising |
| url | http://europepmc.org/articles/PMC6383872?pdf=render |
| work_keys_str_mv | AT takunonomura extendedkalmanfilterbaseddynamicmodedecompositionforsimultaneoussystemidentificationanddenoising AT hisaichishibata extendedkalmanfilterbaseddynamicmodedecompositionforsimultaneoussystemidentificationanddenoising AT ryojitakaki extendedkalmanfilterbaseddynamicmodedecompositionforsimultaneoussystemidentificationanddenoising |