Search

This brief developed a novel sparsity constrained recursive adaptive filtering algorithm via the generalized correntropy criterion with variable center (GMCC-VC) for sparse system parameters estimation. In order to improve the performance of the adaptive filtering algorithm with GMCC under the non-Gaussian and non-zero mean noise environments, the GMCC is extended by adding a center which can be located at any position in the generalized Gaussian kernel function. Specifically, we proposed a novel adaptive online update method to optimize the kernel method and center. In addition, we mainly derived a recursive GMCC-VC algorithm based on the GMCC with the sparse constraint to exploit sparsity. Numerical simulation results are given to show that the proposed algorithm is efficient for sparse system parameter estimation problems in non-Gaussian and non-mean noise environments. [ABSTRACT FROM AUTHOR]

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non Gaussian signal processing and machine learning. In this paper, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important properties. We apply the KRSL to adaptive filtering and investigate the robustness, and then develop the MKRSL algorithm and analyze the mean square convergence performance. Compared with correntropy, the KRSL can offer a more efficient performance surface, thereby enabling a gradient-based method to achieve faster convergence speed and higher accuracy while still maintaining the robustness to outliers. Theoretical analysis results and superior performance of the new algorithm are confirmed by simulation. [ABSTRACT FROM PUBLISHER]

As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel, which is, of course, not always the best choice. In this paper, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel, and present some important properties. We further propose the generalized maximum correntropy criterion (GMCC) and apply it to adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is derived, and the stability problem and steady-state performance are studied. We show that the proposed algorithm is very stable and can achieve zero probability of divergence (POD). Simulation results confirm the theoretical expectations and demonstrate the desirable performance of the new algorithm. [ABSTRACT FROM AUTHOR]

Compared with the least-mean-square algorithm, the least mean $p$th power algorithm shows a better robustness performance against impulsive noises such as the $\alpha$-stable noises. However, it still exhibits slow convergence rate and high kernel misadjustment. To overcome this drawback, a novel recursive logarithmic least mean $p$th (RLLMP) algorithm is proposed for the Volterra system identification under $\alpha$-stable noise environments. Instead of minimizing the $p$th power, the new algorithm aims to minimize the $p$th logarithmic cost, which makes it more robust against impulsive interferences. Furthermore, to enhance tracking performance, an improved variable forgetting factor (IVFF) algorithm (IVFF-RLLMP) is proposed, which is based on the robust estimation of outliers. Simulation results are presented to demonstrate the improved performance of the RLLMP and IVFF-RLLMP. [ABSTRACT FROM AUTHOR]