TY  - JOUR
T1  - Çevrim içi sansürleme tabanlı CLMK algoritmalarının adım büyüklüğü, unutma faktörü ve filtre derecesine göre detaylı başarım analizi
TT  - The detailed performance analysis of online censoring-based CLMK algorithms with step size, forgetting factor, and filter order
AU  - Çolak Güvenç, Buket
AU  - Mengüç, Engin Cemal
PY  - 2024
DA  - July
Y2  - 2024
DO  - 10.28948/ngumuh.1453683
JF  - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
JO  - NÖHÜ Müh. Bilim. Derg.
PB  - Niğde Ömer Halisdemir Üniversitesi
WT  - DergiPark
SN  - 2564-6605
SP  - 892
EP  - 904
VL  - 13
IS  - 3
LA  - tr
AB  - Kompleks-değerli en küçük kurtosis tabanlı (complex-valued least mean kurtosis, CLMK) algoritmalar sağladığı avantajlar nedeniyle son zamanlarda literatürde oldukça popüler bir hale gelmiştir. Bu çalışmada, literatürde daha önce Çolak Güvenç ve Mengüç tarafından önerilen çevrim içi sansürleme tabanlı OC-CLMK, OC-ACLMK, ROC-CLMK ve ROC-ACLMK algoritmalarının adım büyüklüğü, unutma faktörü ve filtre derecesine göre detaylı başarım analizi sunulmuştur. İlk olarak, bu çalışmada yapılan başarım analizi, algoritmaların önerildiği çalışmada kullanılan sistem tanımlama problemine ait iki farklı senaryo üzerinde birbirinden farklı değerlere sahip parametre aralıklarında ve üç farklı sansürleme oranına göre kıyaslanarak yapılmıştır. Ardından, önerilen çevrim içi sansürleme tabanlı CLMK algoritmalarının bu önemli parametrelere olan duyarlılığı kararlı-durum ortalama kare hata (steady-state mean square error, SS-MSE) olarak verilmiştir. Böylece, çevrim içi sansürleme tabanlı CLMK algoritmalarının son kullanıcılarına hangi parametre sınırları içinde çalışılması gerektiğine ilişkin yol gösterici bir çalışma sunulmuştur.
KW  - Parametre analizi
KW  - Çevrim içi sansürleme
KW  - Kompleks-değerli en küçük kurtosis
N2  - Recently, complex-valued least mean kurtosis (CLMK) algorithms have become highly popular in the literature due to the advantages they offer. This study provides a detailed performance analysis of OC-CLMK, OC-ACLMK, ROC-CLMK, and ROC-ACLMK algorithms previously proposed by Çolak Güvenç and Mengüç, focusing on step size, forgetting factor, and filter order. Firstly, the performance analysis in this study is made by comparing parameter ranges with different values and three different censoring ratios on two different scenarios of the system identification problem used in the study in which the algorithms were proposed. Then, the dependencies of the proposed online censoring-based CLMK algorithms to these crucial parameters is presented in terms of steady-state mean square error (SS-MSE). Thus, a guiding study is presented to the end-users of online censoring-based CLMK algorithms regarding the parameter limits within which they should be worked.
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CR  - E. C. Mengüç, Large-scale regression in the complex domain: Performance analysis of CRLS algorithms censoring noninformative data in an online manner. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12 (2), pp.349-359, 2023. https:// doi.org/10.28948/ngumuh.1234303.
CR  - E. C. Mengüç, N. Acır and D.P. Mandic, A Class of Online Censoring Based Quaternion-Valued Least Mean Square Algorithms. IEEE Signal Processing Letters, 30, pp. 244-248, 2023. https://doi.org/10.11 09/LSP.2023.3255000.
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CR  - Y. Eren, B. Çolak Güvenç and E. C. Mengüç, Cost-Effective Acoustic Feedback Cancellers for Digital Hearing Aids, IEEE/ACM Transactions on Audio Speech and Language Processing, 2024. https://doi.org/10.1109/TASLP.2024.3389644.
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CR  - B. Çolak Güvenç, Y. Eren ve E.C. Mengüç, Adaptive Prediction of Financial Data with Complex-Valued Informative Data, 2022 Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı (ELECO), Bursa, Turkey, pp.1-5, 2022.
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UR  - https://doi.org/10.28948/ngumuh.1453683
L1  - https://dergipark.org.tr/tr/download/article-file/3798500
ER  -