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Mikhail Skliar - One of the best experts on this subject based on the ideXlab platform.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    American Control Conference, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman-Bucy filter.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    International Journal of Robust and Nonlinear Control, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.

Michael Basin - One of the best experts on this subject based on the ideXlab platform.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    American Control Conference, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman-Bucy filter.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    International Journal of Robust and Nonlinear Control, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.

Jose P Perez - One of the best experts on this subject based on the ideXlab platform.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    American Control Conference, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman-Bucy filter.

  • optimal filtering for polynomial system States with polynomial multiplicative noise
    International Journal of Robust and Nonlinear Control, 2006
    Co-Authors: Michael Basin, Jose P Perez, Mikhail Skliar
    Abstract:

    In this paper, the optimal filtering problem for polynomial system States with polynomial multiplicative noise over Linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the Stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering Equations for any polynomial State with polynomial multiplicative noise over Linear observations is then established, which yields the explicit closed form of the filtering Equations in the particular cases of a Linear State Equation with Linear multiplicative noise and a biLinear State Equation with biLinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic State with a quadratic multiplicative noise over Linear observations against the optimal filter for a quadratic State with a State-independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.

T. Ozaki - One of the best experts on this subject based on the ideXlab platform.

  • local Linearization filters for non Linear continuous discrete State space models with multiplicative noise
    International Journal of Control, 2003
    Co-Authors: J. C. Jimenez, T. Ozaki
    Abstract:

    In this paper, the local Linearization method for the approximate computation of the prediction and filtering estimates of continuous-discrete State space models is extended to the general case of non-Linear non-autonomous models with multiplicative noise. The approximate prediction and filter estimates are obtained by applying the optimal Linear filter to the piecewise Linear State space model that emerges from a local Linearization of both the non-Linear State Equation and the non-Linear measurement Equation. In addition, the solutions of the differential Equations that describe the evolution of the first two conditional moments between observations are obtained, and an algorithm for their numerical computation is also given. The performance of the LL filters is illustrated by mean of numerical experiments.

Junichi Imura - One of the best experts on this subject based on the ideXlab platform.

  • stabilization of deterministic finite automata based on Linear State Equation representation
    European Control Conference, 2009
    Co-Authors: Koichi Kobayashi, Junichi Imura
    Abstract:

    This paper discusses the State feedback stabilization problem of a deterministic finite automaton (DFA), based on its State Equation representation recently proposed by the authors. First, a Linear State Equation representation of the DFA is briefly explained. Next, after the notion of equilibrium points and stabilizability of the DFA are defined, a necessary and sufficient condition for the DFA to be stabilizable is derived. Then under these preparations, a characterization of all stabilizing State feedback controllers is presented. Finally, a simple example is given to show how to follow the proposed procedure.