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Last Updated : 03/2016
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  • Less conservative stabilization conditions for Markovian jump systems with incomplete knowledge of transition probabilities and input saturation

    Nam Kyu Kwon, Bum Yong Park, PooGyeon Park

    Optimal Control Applications and Methods

    Abstract This paper proposes less conservative stabilization conditions for Markovian jump systems with incompleteknowledge of transition probabilities and input saturation. The transition rates associated with the transitionprobabilities are expressed in terms of three properties, which do not require the lower and upper bounds ofthe transition rates, differently from other approaches in the literature. The resulting conditions are convertedinto the second-order matrix polynomial of the unknown transition rates. The polynomial can be representedas quadratic form of vectorized identity matrices scaled by one and the unknown transition rates. And then,the LMI conditions are obtained from the quadratic form. Also, an optimization problem is formulated to?nd the largest estimate of the domain of attraction in mean square sense of the closed-loop systems. Finally,two numerical examples are provided to illustrate the effectiveness of the derived stabilization conditions.Copyright ⓒ 2016 John Wiley & Sons, Ltd.
  • A diffusion subband adaptive filtering algorithm for distributed estimation using variable step size and new combination method based on the MSD

    Ji-Hye Seo, Sang Mok Jung, PooGyeon Park

    Digital Signal Processing

    Abstract This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors.