سال انتشار: ۱۳۹۰

محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران

تعداد صفحات: ۵

نویسنده(ها):

Amir Rastegarnia – Faculty of Electrical and Computer Engineering, University of Tabriz
Mohammad Ali Tinati –
Azam Khalili –

چکیده:

In this paper, we investigate the effect of observation quality on the steady-state performance of incremental adaptive networks with LMS learning. We exploit the knowledge of observation quality to adjust the step-size parameter in an adaptive network according to nodes observation quality. We formulate the step-size assignment as a constrained optimization problem and then solve it via Lagrange multipliers approach. We show that applying the optimal step sizes in an incremental adaptive network improves its the steady-state performance. The simulation results are also presented to illustrate the derived theoretical results