scholarly journals A Fast Quasi-Newton Adaptive Algorithm Based on Approximate Inversion of the Autocorrelation Matrix

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 47877-47887
Author(s):  
Mohammad Shukri Salman ◽  
Osman Kukrer ◽  
Aykut Hocanin
Keyword(s):  
Adaptive Algorithm ◽  
Autocorrelation Matrix ◽  
Quasi Newton

scholarly journals Quasi-Newton least-mean fourth adaptive algorithm

2017 ◽  
Author(s):  
Umair bin Mansoor ◽  
Qadri Mayyala ◽  
Muhammad Moinuddin ◽  
Azzedine Zerguine
Keyword(s):  
Adaptive Algorithm ◽  
Quasi Newton

H∞ bounds for quasi-Newton adaptive algorithm

2009 ◽  
Vol 89 (11) ◽  
pp. 2304-2309
Author(s):  
N. Kalyanasundaram ◽  
Abhishek Jindal ◽  
Anindya Gupta
Keyword(s):  
Adaptive Algorithm ◽  
Quasi Newton

scholarly journals Discrete wavelet transform-based RI adaptive algorithm for system identification

2020 ◽  
Vol 10 (3) ◽  
pp. 2383
Author(s):  
Mohammad Shukri Salman ◽  
Alaa Eleyan ◽  
Bahaa Al-Sheikh
Keyword(s):  
Wavelet Transform ◽  
System Identification ◽  
Convergence Rate ◽  
Discrete Wavelet Transform ◽  
Adaptive Algorithm ◽  
Convergence Rates ◽  
Recursive Least Squares ◽  
Discrete Wavelet ◽  
Step Size ◽  
Autocorrelation Matrix

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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