2017/11/23 by Wentao Ma, Dongqiao Zheng, Ma, Wentao +7
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1711.08677
openalex publication_date 2017/11/23 · openalex created_date 2017/12/04 · openalex updated_date 2026/07/31
This paper proposed 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 (BCV) to the NMCC algorithm, and then an unbiasedness criterion and some reasonable assumptions are used to compute the BCV. Taking advantage of the BCV, 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.