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This paper introduces a data-adaptive Kronecker filtering framework based on the data-reusing sampled-function weighted order (KSFWO) and switching KSFWO filters by means of data-reusing least mean square (DR-LMS) algorithm. The data-reusing algorithm is introduced and parameterized by the number of reuses of each weight update per data sample. We propose the adaptive KSFWO and switching KSFWO filters based on DR-LMS algorithm with the smoothing and robust characteristics. The coefficients of proposed filters are the samples of bounded real-valued function. These filters can be designed in form of a stochastic gradient filter. The proposed filters can be performed the robust smoothing filtering in some applications.
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Faculty of Engineering and Technology
Mahanakorn University of Technology
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