Stack filters are a large class of nonlinear filters that include median and ranked-order filters. Usually, these filters are analyzed in terms of their invariant signals and their convergence behavior. Convergence refers to whether iterative application of a stack filter to a signal will make it converge to an invariant signal. Previous convergence results have been deterministic results for limited classes of stack filters. In these results, the "visiting strategy" for the filter kernel as it filters a signal is arbitrary, but known a priori. Here, we use a new stochastic approach, with randomly evolving visiting strategies, that applies to all stack filters. We show that, within this framework, all stack filters make all input signals converge almost surely. A result for Boolean networks follows as a corollary.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Stochastic convergence of stack filters and Boolean networks


    Contributors:
    Wendt, P. (author)


    Publication date :

    1994-01-01


    Size :

    413401 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Stochastic Convergence of Stack Filters and Boolean Networks

    Wendt, P. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994


    Iterative Algorithms and Its Convergence of M-Filters for Stochastic System

    Hu, F. / Sun, G. | British Library Online Contents | 1998


    Statistical design of stack filters (Invited Paper) [3457-01]

    Astola, J. T. / Kuosmanen, P. / SPIE | British Library Conference Proceedings | 1998


    Using genetic algorithm optimizing stack filters based on MMSE criterion

    Zhao, C. / Zhang, W. | British Library Online Contents | 2005


    STOCHASTIC CONVERGENCE ACROSS U.S. STATES

    Mello, M. | British Library Online Contents | 2011