scholarly journals A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Young-Seok Choi
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Sparse System ◽  
Subband Adaptive Filter ◽  
Sparse System Identification ◽  
The Cost ◽  
Norm Penalty

This paper presents a novel subband adaptive filter (SAF) for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses ofl1-norm optimization andl0-norm penalty of the weight vector in the cost function, the proposedl0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposedl0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.

scholarly journals Subband Adaptive Filtering withl1-Norm Constraint for Sparse System Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Young-Seok Choi
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Sparse System ◽  
Gradient Based ◽  
Subband Adaptive Filter ◽  
Sparse System Identification ◽  
Norm Constraint ◽  
Optimum Solution ◽  
The Cost ◽  
Normalized Subband Adaptive Filter

This paper presents a new approach of the normalized subband adaptive filter (NSAF) which directly exploits the sparsity condition of an underlying system for sparse system identification. The proposed NSAF integrates a weightedl1-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weightedl1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weightedl1-norm regularized NSAF. The choice of distinct weightedl1-norm regularization leads to two versions of thel1-norm regularized NSAF. Numerical results clearly indicate the superior convergence of thel1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.

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.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

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