Robust sparse linear regression for tokamak plasma boundary estimation using variational Bayes

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F18%3A00491728" target="_blank" >RIV/61389021:_____/18:00491728 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/18:00491728 RIV/68407700:21230/18:00324830 RIV/68407700:21340/18:00324830
Result on the web
<a href="http://dx.doi.org/10.1088/1742-6596/1047/1/012015" target="_blank" >http://dx.doi.org/10.1088/1742-6596/1047/1/012015</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/1047/1/012015" target="_blank" >10.1088/1742-6596/1047/1/012015</a>

Result language
angličtina
Original language name
Robust sparse linear regression for tokamak plasma boundary estimation using variational Bayes
Original language description
Precise control of the shape of plasma in a tokamak requires reliable reconstruction of the plasma boundary. The problem of boundary estimation can be reduced to a simple linear regression with a potentially infinite amount of regressors. This regression problem poses some difficulties for classical methods. The selection of regressors significantly influences the reconstructed boundary. Also, the underlying model may not be valid during certain phases of the plasma discharge. Formal model structure estimation technique based on the automatic relevance principle yields a version of sparse least squares estimator. In this contribution, we extend the previous method by relaxing the assumption of Gaussian noise and using Student’s t-distribution instead. Such a model is less sensitive to potential outliers in the measurement. We show on simulations and real data that the proposed modification improves estimation of the plasma boundary in some stages of a plasma discharge. Performance of the resulting algorithm is evaluated with respect to a more detailed and computationally costly model which is considered to be the „ground truth“. The results are also compared to those of Lasso and Tikhonov regularization techniques.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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