Bias-compensated normalized maximum correntropy criterion algorithm for system identification with noisy input.

Highlights • We propose a bias-compensated normalized MCC algorithm which is efficient for the noisy input and impulsive output noise cases. • We use the unbiasedness criterion to derive the bias compensated term for the proposed algorithm. • The proposed BCNMCC algorithm performs better than the original NMCC algorithm in noisy input environment. Abstract This paper proposes a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based cost function, which is rather robust with respect to impulsive noises. To deal with the noisy input, we introduce a bias-compensated vector to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the bias-compensated vector. Taking advantage of the bias-compensated vector, the bias caused by the input noise can be effectively suppressed. System identification simulation results demonstrate that the proposed BCNMCC algorithm can outperform other related algorithms with noisy input especially in an impulsive output noise environment. [ABSTRACT FROM AUTHOR]