Dr. Yingying Zhu Profile

Dr. Yingying Zhu

at Southwest Jiaotong Univ

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Publications (2)

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Proceedings Article | 20 January 2021 Paper

Filtered-s normalized maximum mixture correntropy criterion algorithm for nonlinear active noise control

Pucha Song, Haiquan Zhao, Yingying Zhu

Proceedings Volume 11719, 1171911 (2021) https://doi.org/10.1117/12.2589324

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In order to deal with impulsive noise, the traditional filtered-s normalized maximum correntropy criterion (FsNMCC) adaptive algorithm has good robustness in nonlinear active noise control (ANC) systems. However, the FsNMCC algorithm has a single Gaussian kernel, of which the noise reduction performance is susceptible to the value of the kernel width. To surmount this shortcoming, the filtered-s normalized maximum mixture correntropy criterion (FsNMMCC) algorithm is designed for a functional link artificial neural network (FLANN) based on ANC systems. Simulation results show that the proposed FsNMMCC algorithm in this paper has better noise reduction performance than the FsNMCC algorithm in active noise control of impulsive noise with standard symmetric α-stable (SαS) distribution.

Proceedings Article | 20 January 2021 Paper

Sparse least mean fourth adaptive algorithm for censored regression

Bing Chen, Haiquan Zhao, Yingying Zhu

Proceedings Volume 11719, 117190S (2021) https://doi.org/10.1117/12.2588927

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In the linear systems, the conventional least mean fourth (LMF) algorithm has faster convergence and lower steady-state error than LMS algorithm, However, in many applications, the censored observations occur frequently. In this paper, a least mean fourth (LMF) algorithm with censored regression is proposed for adaptive filtering. When the identified system possesses a certain extent of sparsity, the least mean fourth algorithm for Censored Regression (CRLMF) algorithm may encounter performance degradation. Therefore, a reweighted zero-attracting LMF algorithm based on the censored regression model (RZA-CRLMF) is proposed further. Simulations are carried out in system identification and echo cancellation scenarios. The results verify the effectiveness of the proposed CRLMF and RZA-CRLMF algorithms. Moreover, in sparse system, the RZA-CRLMF algorithm improves further the filter performance in terms of the convergence speed and the mean squared deviation for the presence of sub-Gaussian noise.

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