Mixture of set membership filters approach for big data signal processing

Date

2016

Authors

Kılıç, O. Fatih
Sayın, M. Ömer
Delibalta, İ.
Kozat, Süleyman Serdar

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

BUIR Usage Stats
1
views
3
downloads

Citation Stats

Series

Abstract

In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve significantly reduced computational load for the mixture methods with better performance in convergence rate and steady-state error with respect to conventional mixture methods. Finally, we approve these statements with the simulations done on produce data.

Source Title

Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016

Publisher

IEEE

Course

Other identifiers

Book Title

Degree Discipline

Degree Level

Degree Name

Citation

Published Version (Please cite this version)

Language

Turkish