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Your search keyword '"Zheng, Dongqiao"' showing total 20 results
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20 results on '"Zheng, Dongqiao"'

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

Author

Ma, Wentao, Zheng, Dongqiao, Li, Yuanhao, Zhang, Zhiyu, and Chen, Badong

Subjects
Statistics - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
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., Comment: 14 pages, 4 figures

Published
2017

14. Bias compensated normalized least mean fourth algorithm with correntropy induced metric constraint

Author

Hu Xianzhi, Qiu Jinzhe, Ma Wentao, Zheng Dongqiao, and Zhang Zhiyu

Subjects
Noise, Kernel (linear algebra), Noise measurement, Computer science, Norm (mathematics), 020208 electrical & electronic engineering, 0202 electrical engineering, electronic engineering, information engineering, System identification, Approximation algorithm, 020206 networking & telecommunications, 02 engineering and technology, Induced metric, Algorithm
Abstract

Sparse system identification (SSI) is one of the most important problems in broadband wireless communication systems. At present, the sparse aware adaptive filtering algorithms have been developed for SSI. Considering the noisy input and output noise with non-Gausssian feature case, we propose a new sparse normalized least mean fourth (NLMF) algorithm to address this problem in SSI. The bias-compensated NLMF algorithm (BCNLMF) using the unbiasedness criterion can reduce the interference of the input noise. We introduce the correntropy induced metric (CIM) as an approximation of the $l_{0}$ -norm into the BCNLMF to develop a novel sparse BCNLMF (BC-CIM-NLMF) algorithm which can efficiently solve the SSI under the noisy input and output noise environments. Simulation results show that the proposed algorithm outperforms other existing sparse aware algorithms for SSI.

Published
2018

20. Robust proportionate adaptive filter based on maximum correntropy criterion for sparse system identification in impulsive noise environments.

Author

Ma, Wentao, Zheng, Dongqiao, Zhang, Zhiyu, Duan, Jiandong, and Chen, Badong

Abstract

Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to sparse system identification. The major drawback of the traditional PtAF algorithms based on the mean square error (MSE) criterion show poor robustness in the presence of impulsive noises or abrupt changes because MSE is only valid and rational under Gaussian assumption. However, this assumption is not satisfied in most real-world applications. To improve its robustness under non-Gaussian environments, we incorporate the maximum correntropy criterion (MCC) into the update equation of the PtAF to develop proportionate MCC (PMCC) algorithm. The mean and mean square convergence performance analysis are also performed. Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed PMCC exhibits outstanding performance under the impulsive noise environments. [ABSTRACT FROM AUTHOR]

Published
2018
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