sssMOR Toolbox

バージョン 2.0 (9.6 MB) 作成者: MORLab @ Chair of Automatic Control
sssMOR is a MATLAB toolbox for model order reduction of large-scale dynamical systems in state-space
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更新 2017/9/11

ライセンスの表示

The sssMOR Toolbox implements both classic and state-of-the-art model reduction algorithms that capture the relevant dynamics of the high order system in models of significantly lower dimension.
After installation, execute "runme" for initial setup and download of benchmark models and third-party software.

sssMOR is now available also on GitHub under https://github.com/MORLab/sssMOR.

引用

MORLab @ Chair of Automatic Control (2025). sssMOR Toolbox (https://jp.mathworks.com/matlabcentral/fileexchange/59169-sssmor-toolbox), MATLAB Central File Exchange. に取得済み.

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作成: R2016b
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sss/demos/

sss/src/+sssFunc/

sss/src/@sss/

sss/src/extras/

sss/src/extras/third-party/

sss/src/sim/

sssMOR/demos/

sssMOR/src/MOR/@ssRed/

sssMOR/src/MOR/classic/

sssMOR/src/MOR/stateOfTheArt/

sssMOR/src/extras/

バージョン 公開済み リリース ノート
2.0

- updated toolbox description with comment on "runme" execution after installation
After installation, execute "runme" for setup.

+ Update the ssRed class
+ Updated irka, cirka, cure, rkIcop, ...
+ Added stabsep and l2norm computations for ssRed objects
+ Added publishDoc.p and publishFunction.p
1.8.0.0

- added CIRKA, fast H2-optimal model reduction based on the model function framework (cp. http://ieeexplore.ieee.org/document/7810578/)
- balancing truncation: unified definition of Cholesky (like) factors of the Gramians
- modalMor more efficient
1.7.0.0

- improved compatibility with ss-class definition in the Control System Toolbox (that changed from 2015b to 2016a)
- minor improvements in the reduction routines
(see also the CHANGELOG provided with the Toolbox)
1.6.0.0

the upload of the new toolbox file did not work in the last update
minor bugfix to make compatible with R2016a

Between 2015b and 2016a MATLAB has changed the definition of ss-class properties. In order to make it work correctly in the ssRed class (reduced order models) a few workarounds had to be implemented.