• A Bayesian approach for model identification of LPV systems with uncertain scheduling variables 

      Abbasi, F.; Mohammadpour, J.; Toth, R.; Meskin, Nader ( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)
      This paper presents a Gaussian Process (GP) based Bayesian method that takes into account the effect of additive noise on the scheduling variables for identification of linear parameter-varying (LPV) models in input-output ...
    • A support vector machine-based method for LPV-ARX identification with noisy scheduling parameters 

      Abbasi, F.; Mohammadpour, J.; Toth, R.; Meskin, Nader ( Institute of Electrical and Electronics Engineers Inc. , 2014 , Conference Paper)
      In this paper, we present a method that utilizes support vector machines (SVM) to identify linear parameter-varying (LPV) auto-regressive exogenous input (ARX) models corrupted by not only noise, but also uncertainties in ...
    • State-space LPV model identification using kernelized machine learning 

      Rizvi S.Z.; Velni J.M.; Abbasi F.; T?th R.; Meskin N. ( Elsevier Ltd , 2018 , Article)
      This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical ...