The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Petre Stoica - One of the best experts on this subject based on the ideXlab platform.

  • recursive nonlinear system identification using latent variables
    Automatica, 2018
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear system models with multiple outputs and inputs. We begin by modeling the errors of a Nominal Predictor of the system using a latent variable ...

  • recursive nonlinear system identification using latent variables
    arXiv: Machine Learning, 2016
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a Nominal Predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.

Per Mattsson - One of the best experts on this subject based on the ideXlab platform.

  • recursive nonlinear system identification using latent variables
    Automatica, 2018
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear system models with multiple outputs and inputs. We begin by modeling the errors of a Nominal Predictor of the system using a latent variable ...

  • recursive nonlinear system identification using latent variables
    arXiv: Machine Learning, 2016
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a Nominal Predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.

Dave Zachariah - One of the best experts on this subject based on the ideXlab platform.

  • recursive nonlinear system identification using latent variables
    Automatica, 2018
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear system models with multiple outputs and inputs. We begin by modeling the errors of a Nominal Predictor of the system using a latent variable ...

  • recursive nonlinear system identification using latent variables
    arXiv: Machine Learning, 2016
    Co-Authors: Per Mattsson, Dave Zachariah, Petre Stoica
    Abstract:

    In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a Nominal Predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization-minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.