scholarly journals Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input

Entropy ◽  
2018 ◽  
Vol 20 (6) ◽  
pp. 407 ◽  
Author(s):  
Wentao Ma ◽  
Dongqiao Zheng ◽  
Zhiyu Zhang ◽  
Jiandong Duan ◽  
Jinzhe Qiu ◽  
...  
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Filtering Algorithms ◽  
Sparse System ◽  
Noisy Input ◽  
Sparse System Identification ◽  
Maximum Correntropy Criterion

Dynamic RLS-DCD for Sparse System Identification

2014 ◽  
Vol 602-605 ◽  
pp. 2411-2414
Author(s):  
Qing Xia ◽  
Yun Lin ◽  
Hui Luo
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Coordinate Descent ◽  
Identification Performance ◽  
Computationally Efficient ◽  
Filtering Algorithm ◽  
Sparse System ◽  
Sparse System Identification ◽  
Better Than

In this passage we propose a computationally efficient adaptive filtering algorithm for sparse system identification.The algorithm is based on dichotomous coordinate descent iterations, reweighting iterations,iterative support detection.In order to reduce the complexity we try to discuss in the support.we suppose the support is partial,and partly erroneous.Then we can use the iterative support detection to solve the problem.Numerical examples show that the proposed method achieves an identification performance better than that of advanced sparse adaptive filters (l1-RLS,l0-RLS) and its performance is close to the oracle performance.

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