123 research outputs found
Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning
This paper proposes a robust adaptive algorithm for smooth graph signal
recovery which is based on generalized correntropy. A proper cost function is
defined for this purpose. The proposed algorithm is derived and a kernel width
learning-based version of the algorithm is suggested which the simulation
results show the superiority of it to the fixed correntropy kernel version of
the algorithm. Moreover, some theoretical analysis of the proposed algorithm
are provided. In this regard, firstly, the convexity analysis of the cost
function is discussed. Secondly, the uniform stability of the algorithm is
investigated. Thirdly, the mean convergence analysis is also added. Finally,
the complexity analysis of the algorithm is incorporated. In addition, some
synthetic and real-world experiments show the advantage of the proposed
algorithm in comparison to some other adaptive algorithms in the literature of
adaptive graph signal recovery
Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm
Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters
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