Yazar "Mengüç E.C." seçeneğine göre listele

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    An augmented complex-valued least-mean kurtosis algorithm for the filtering of noncircular signals
    (Institute of Electrical and Electronics Engineers Inc., 2018) Mengüç E.C.; Acır N.
    In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms. © 2017 IEEE.
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    Complex-valued least mean Kurtosis adaptive filter algorithm [Kompleks-Degerli En Küçük Ortalama Kurtosis Adaptif Filtre Algoritmasi]
    (Institute of Electrical and Electronics Engineers Inc., 2016) Mengüç E.C.; Acir N.
    In this study, a complex-valued least mean Kurtosis (CLMK) adaptive filter algorithm is designed for processing complex-valued signals. The performance of the designed algorithm is tested on a complex-valued system identification and compared the complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) algorithms. As a result, the CLMK algorithm shows a higher performance than the other algorithms in terms of the convergence rate, mean square error (MSE) and mean square deviation (MSD). © 2016 IEEE.
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    Novel quaternion-valued least-mean kurtosis adaptive filtering algorithm based on the GHR calculus
    (Institution of Engineering and Technology, 2018) Mengüç E.C.
    A novel quaternion-valued least-mean kurtosis (QLMK) adaptive filtering algorithm is proposed for three- and fourdimensional processes by using the recent generalised Hamilton-real (GHR) calculus. The proposed QLMK algorithm based GHR calculus minimises the negated kurtosis of the error signal as a cost function in the quaternion domain, thus provides an elegant way to solve a trade-off problem between the convergence rate and steady-state error. Moreover, the proposed QLMK algorithm has naturally a robust behaviour for a wide range of noise signals due to its kurtosis-based cost function. Furthermore, the steady-state performance of the proposed QLMK algorithm is analysed to obtain convergence and misadjustment conditions. The comprehensive simulation results on benchmark and real-world problems show that the use of this cost function defined by the quaternion statistics in the proposed QLMK algorithm allows us to process quaternion-valued signals and thus, significantly enhances the performance of the adaptive filter in terms of both the steady-state error and the convergence rate, as compared with the quaternion-valued least-mean-square algorithm based on the recent GHR calculus. © 2018, The Institution of Engineering and Technology.