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Recursive Maximum Correntropy Learning Algorithm With Adaptive Kernel Size
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DOI：10.1109/TCSII.2017.2778038.png)
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Abstract
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In this brief, a robust correntropy-based adaptive learning algorithm, called the adaptive kernel recursive maximum correntropy criterion, is proposed by considering an adaptive kernel size based on the Kullback-Leibler divergence minimization. Simulation results confirm that the proposed algorithm can perform better than other adaptive algorithms especially when the environment is disturbed by non-Gaussian noises. The proposed algorithm is beneficial for using in helicopters, since the pilot speech signal is contaminated by the acoustic impulsive noises that are originated from rotor blades.
Keywords:
Adaptive algorithm
correntropy criterion
non-Gaussian noise
adaptive kernel size
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