State space LPV model identification using LS-SVM: A case-study with dynamic dependence | IEEE Conference Publication | IEEE Xplore

State space LPV model identification using LS-SVM: A case-study with dynamic dependence


Abstract:

In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is...Show More

Abstract:

In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.
Date of Conference: 19-22 September 2016
Date Added to IEEE Xplore: 13 October 2016
ISBN Information:
Conference Location: Buenos Aires, Argentina

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