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- W2336777494 abstract "grusal kestirim problemi incelenmektedir. Maliyet fonksiyonu olarak ortalama karesel hata (MSE) du¸ sunulmu¸ stur ve sinirli belirsizlikler altinda gurbuz bir algoritma onerilmistir. Sunulan yontem yarismaci algoritma yapisina sahiptir ve bu yapiya ulasmak icin do˘ grusal kestiricinin performansi, bilinmeyen veri belirsizliklerine gore ayarlanmis do˘ grusal enkucuk MSE (MMSE) kestiricisinin performansina goreceli olarak tanimlanmistir. Daha sonra, bu goreceli performans olcutunu en kotu durumdaki sistem modeline gore enkuculten do˘ grusal kestirici bulunmustur. Bu yarismaci kestiriciyi bulmak icin cozulmesi gereken problemin yari-kesin programlama (SDP) problemi olarak du¸ sunulebilece˘ gi gosterilmistir. Ayrica, teorik sonuclari izah etmek icin sayisal ornekler sunulmustur. Anahtar Kelimeler—yarismaci, gurbuz, do˘ grusal kestirim, veri belirsizlikleri. Abstract—In this paper, we consider the linear estimation problem under structured data uncertainties. A robust algorithm is presented under bounded uncertainties under the mean square error (MSE) criterion. The performance of the linear estimator is defined relative to the performance of the linear minimum MSE (MMSE) estimator tuned to the underlying unknown data uncertainties, i.e., the introduced algorithm has a competitive framework. Then, using this relative performance measure, we find the estimator that minimizes this cost for the worst-case sys- tem model. We show that finding this estimator can equivalently be cast as a semidefinite programming (SDP) problem. Numerical examples are provided to illustrate the theoretical results." @default.
- W2336777494 created "2016-06-24" @default.
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- W2336777494 date "2014-01-01" @default.
- W2336777494 modified "2023-09-27" @default.
- W2336777494 title "Yapisal Veri Belirsizlikleri Altinda Yarismaci Do˘ grusal MMSE Kestirim Competitive Linear MMSE Estimation Under Structured Data Uncertainties" @default.
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