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To solve the conflicting requirement of fast convergence and low steady-state misalignment, a variable step-size shrinkage set-membership affine projection algorithm is proposed, which is efficient for the correlated input signal and noisy input environments. The new variable step size is derived by minimizing the square of noise-free a posteriori error, and the shrinkage method is employed to estimate the second-order statistics of the noise-free a priori error vector. Moreover, the stability analysis of the algorithm is conducted. Simulations demonstrate the effectiveness of the proposed algorithm for various noisy input environments. [ABSTRACT FROM AUTHOR]

In this paper, the Correntropy Induced Metric (CIM) as an alternative to the well-known mean square error (MSE) is employed in Chebyshev functional link artificial neural network (CFLANN) to deal with the noisy training data set and enhance the generalization performance. The MSE performs well under Gaussian noise but it is sensitive to large outliers. The CIM as a local similarity measure, however, can improve significantly the anti-noise ability of CFLANN. The convergence of the proposed algorithm, namely the CFLANN based on CIM (CFLANNCIM), has been analyzed. Simulation results on nonlinear channel identification show that CFLANNCIM can perform much better than the traditional CFLANN and multiple-layer perceptron (MLP) neural networks trained under MSE criterion. [ABSTRACT FROM AUTHOR]

A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price's theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. [ABSTRACT FROM AUTHOR]

To improve the robustness of subband adaptive filter (SAF) against impulsive interferences, we propose two modified SAF algorithms with an individual scale function for each subband, which are derived by maximizing correntropy-based cost function and minimizing logarithm-based cost function, respectively, called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the subband scale functions can sharply drop the step size, which eliminate the influence of outliers on the tap-weight vector update. Therefore, the proposed algorithms are robust against impulsive interferences and exhibit the faster convergence rate and better tracking capability than the sign SAF (SSAF) algorithm. Besides, in impulse-free interference environments, the proposed algorithms achieve similar convergence performance as the normalized SAF (NSAF) algorithm. Simulation results have demonstrated the performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an $$l_{0}$$ -norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with $$l_{1}$$ -norm or reweighted $$l_{1}$$ -norm constraints. [ABSTRACT FROM AUTHOR]