Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input

TL;DR

This paper introduces a bias-compensated normalized maximum correntropy criterion algorithm designed for system identification in noisy environments, effectively reducing bias caused by input noise and outperforming existing methods especially under impulsive output noise.

Contribution

The paper presents a novel bias-compensated NMCC algorithm that effectively suppresses input noise bias in system identification tasks.

Findings

Outperforms existing algorithms with noisy input

Reduces steady-state misalignment in impulsive noise environments

Effective bias suppression due to bias-compensated vector

Abstract

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.