سال انتشار: ۱۳۸۵

محل انتشار: دوازدهمین کنفرانس سالانه انجمن کامپیوتر ایران

تعداد صفحات: ۴

نویسنده(ها):

Hadi Sadoghi Yazdi – Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran
Seyed Ebrahim Hosseini – Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Iran
Mohammad Reza Hosseini – Department of physical education and sport science, Tarbiat Moallem University of Sabzevar

چکیده:

This paper is concerned with studying the forgetting factor of the recursive least square (RLS). A new dynamic forgetting factor (DFF) for RLS algorithm is presented. The proposed DFF-RLS is compared to other methods. Better performance at convergence and tracking of noisy chirpsinusoid is achieved. The control of the forgetting factor at DFF-RLS is based on the gradient of inverse correlation matrix. Compared with the
gradient of mean square error algorithm, the proposed approach provides faster tracking and smaller mean square error. In low signal-to-noise ratios, the performance of the proposed method is superior to other approaches.