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

arXiv: https://arxiv.org/abs/1711.08677 · HTML: https://mlsanity.com/paper/1711.08677/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Adaptive sparseness for correntropy-based robust regression via automatic relevance determination](https://mlsanity.com/paper/2302.00082/) (2023)
- [Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging](https://mlsanity.com/paper/2408.14843/) (2024)
- [Nonlinear Bias-Compensated Adaptive Filter and Its Application for Time-Series Prediction](https://mlsanity.com/paper/2607.19902/) (2026)

## Abstract (arXiv)

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.