The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform

Guan Gui - One of the best experts on this subject based on the ideXlab platform.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    International Conference on Digital Signal Processing, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized least mean p-power (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise synchronously, this paper proposes two effective NLMP-type algorithms. The first one is correntropy induced metric (CIM) Constraint NLMP (CIMNLMP) algorithm. The second one is an improved CIM Constraint Variable regularized NLMP (CIMVRNLMP) algorithm, in which Variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error. Numerical simulations are given to confirm the two proposed algorithms.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    arXiv: Information Theory, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) Constraint based NLMP (CIMNLMP). Based on the first proposed algorithm, moreover, we propose an improved CIM Constraint Variable regularized NLMP(CIMVRNLMP) algorithm by utilizing Variable regularized parameter(VRP) selection method which can further adjust convergence speed and steady-state error. Numerical simulations are given to confirm the proposed algorithms.

Jihong Zhao - One of the best experts on this subject based on the ideXlab platform.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    International Conference on Digital Signal Processing, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized least mean p-power (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise synchronously, this paper proposes two effective NLMP-type algorithms. The first one is correntropy induced metric (CIM) Constraint NLMP (CIMNLMP) algorithm. The second one is an improved CIM Constraint Variable regularized NLMP (CIMVRNLMP) algorithm, in which Variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error. Numerical simulations are given to confirm the two proposed algorithms.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    arXiv: Information Theory, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) Constraint based NLMP (CIMNLMP). Based on the first proposed algorithm, moreover, we propose an improved CIM Constraint Variable regularized NLMP(CIMVRNLMP) algorithm by utilizing Variable regularized parameter(VRP) selection method which can further adjust convergence speed and steady-state error. Numerical simulations are given to confirm the proposed algorithms.

Badong Chen - One of the best experts on this subject based on the ideXlab platform.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    International Conference on Digital Signal Processing, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized least mean p-power (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise synchronously, this paper proposes two effective NLMP-type algorithms. The first one is correntropy induced metric (CIM) Constraint NLMP (CIMNLMP) algorithm. The second one is an improved CIM Constraint Variable regularized NLMP (CIMVRNLMP) algorithm, in which Variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error. Numerical simulations are given to confirm the two proposed algorithms.

  • sparsity aware normalized least mean p power algorithms with correntropy induced metric penalty
    arXiv: Information Theory, 2015
    Co-Authors: Jihong Zhao, Badong Chen, Guan Gui
    Abstract:

    For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is without considering the inherent sparse structure distribution of unknown system. To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) Constraint based NLMP (CIMNLMP). Based on the first proposed algorithm, moreover, we propose an improved CIM Constraint Variable regularized NLMP(CIMVRNLMP) algorithm by utilizing Variable regularized parameter(VRP) selection method which can further adjust convergence speed and steady-state error. Numerical simulations are given to confirm the proposed algorithms.

Priyank Kalla - One of the best experts on this subject based on the ideXlab platform.

  • Variable ordering for efficient sat search by analyzing Constraint Variable dependencies
    Theory and Applications of Satisfiability Testing, 2005
    Co-Authors: Vijay Durairaj, Priyank Kalla
    Abstract:

    This paper presents a new technique to derive an initial static Variable ordering for efficient SAT search. Our approach not only exploits Variable activity and connectivity information simultaneously, but it also analyzes how tightly the Variables are related to each other. For this purpose, a new metric is proposed – the degree of correlation among pairs of Variables. Variable activity and correlation information is modeled (implicitly) as a weighted graph. A topological analysis of this graph generates an order for SAT search. Also, the effect of decision-assignments on clause-Variable dependencies is taken into account during this analysis. An algorithm called ACCORD (ACtivity – CORrelation – ORDering) is proposed for this purpose. Using efficient implementations of the above, experiments are conducted over a wide range of benchmarks. The results demonstrate that: (i) the Variable order generated by our approach significantly improves the performance of SAT solvers; (ii) time to derive this order is a fraction of the overall solving time. As a result, our approach delivers faster performance as compared to contemporary approaches.

Vijay Durairaj - One of the best experts on this subject based on the ideXlab platform.

  • Variable ordering for efficient sat search by analyzing Constraint Variable dependencies
    Theory and Applications of Satisfiability Testing, 2005
    Co-Authors: Vijay Durairaj, Priyank Kalla
    Abstract:

    This paper presents a new technique to derive an initial static Variable ordering for efficient SAT search. Our approach not only exploits Variable activity and connectivity information simultaneously, but it also analyzes how tightly the Variables are related to each other. For this purpose, a new metric is proposed – the degree of correlation among pairs of Variables. Variable activity and correlation information is modeled (implicitly) as a weighted graph. A topological analysis of this graph generates an order for SAT search. Also, the effect of decision-assignments on clause-Variable dependencies is taken into account during this analysis. An algorithm called ACCORD (ACtivity – CORrelation – ORDering) is proposed for this purpose. Using efficient implementations of the above, experiments are conducted over a wide range of benchmarks. The results demonstrate that: (i) the Variable order generated by our approach significantly improves the performance of SAT solvers; (ii) time to derive this order is a fraction of the overall solving time. As a result, our approach delivers faster performance as compared to contemporary approaches.