Browsing by Subject "Adaptive filtering algorithms"

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    Boosted LMS-based piecewise linear adaptive filters
    (IEEE, 2016) Kari, Dariush; Marivani, Iman; Delibalta, İ.; Kozat, Süleyman Serdar
    We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we have several adaptive constituent filters that run in parallel. For each newly received input vector and observation pair, each filter adapts itself based on the performance of the other adaptive filters in the mixture on this current data pair. These relative updates provide the boosting effect such that the filters in the mixture learn a different attribute of the data providing diversity. The outputs of these constituent filters are then combined using adaptive mixture approaches. We provide the computational complexity bounds for the boosted adaptive filters. The introduced methods demonstrate improvement in the performances of conventional adaptive filtering algorithms due to the boosting effect.
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    Improved convergence performance of adaptive algorithms through logarithmic cost
    (IEEE, 2014-05) Sayın, Muhammed O.; Vanlı, N. Denizcan; Kozat, Süleyman S.
    We present a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce the least mean logarithmic square (LMLS) algorithm that achieves comparable convergence performance with the least mean fourth (LMF) algorithm and overcomes the stability issues of the LMF algorithm. In addition, we introduce the least logarithmic absolute difference (LLAD) algorithm. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interference and outperforms the sign algorithm (SA). © 2014 IEEE.