Transient Analysis of the Set-Membership LMS Algorithm

2021 ◽  
pp. 1-1
Author(s):  
Newton N. Siqueira ◽  
Tiago P. Ferreira ◽  
Leonardo C. Resende ◽  
Rodrigo M. S. Pimenta ◽  
Diego B. Haddad ◽  
...  
2021 ◽  
Vol 36 (1) ◽  
pp. 114-118
Author(s):  
Rodrigo Pimenta ◽  
Newton Siqueira ◽  
Mariane Petraglia ◽  
Diego Haddad

Author(s):  
Hamid Reza Moradi ◽  
Akram Zardadi

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.


1998 ◽  
Vol 5 (5) ◽  
pp. 111-114 ◽  
Author(s):  
S. Gollamudi ◽  
S. Nagaraj ◽  
S. Kapoor ◽  
Yih-Fang Huang

Author(s):  
Assunta Andreozzi ◽  
Bernardo Buonomo ◽  
Oronzio Manca ◽  
Salvatore Tamburrino

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