Sparse System Identification in The Presence of Noisy Input Signal Using Biased Compensator Minimum Error Entropy Algorithm

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Sparse System Identification in The Presence of Noisy Input Signal Using Biased Compensator Minimum Error Entropy Algorithm

机译：使用偏置补偿器最小误差熵算法在存在嘈杂输入信号时稀疏系统识别

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Sparse systems are identified effectively by correntropy induced minimum error entropy (CIM-MEE) algorithm in the presence of non-Gaussian noise. But this does not take into account the noisy input signal. This paper presents a new approach for sparse system identification having input signal corrupted by white Gaussian noise. The noisy input signal produces a bias during estimation. The proposed scheme incorporates a bias compensator to overcome this bias by adding a constraint in objective function of CIM-MEE algorithm. Simulations carried out in MATLAB confirm better performance of proposed Biased Compensator CIM-MEE (BC-CIM-MEE) algorithm for noisy input signal in the presence of impulsive measurement noise.

机译：通过控制在存在非高斯噪声的情况下有效地识别稀疏系统。但这没有考虑到嘈杂的输入信号。本文提出了一种新的稀疏系统识别方法，其具有白色高斯噪声损坏的输入信号。噪声输入信号在估计期间产生偏差。所提出的方案包括偏置补偿器，通过在CIM-MEE算法的目标函数中添加约束来克服该偏差。在MATLAB中进行的模拟确认了在存在冲动测量噪声的情况下，在存在嘈杂输入信号的提出的偏置补偿器CIM-MEE（BC-CIM-MEE）算法的情况下表现出更好的性能。

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《WSEAS Transactions on Signal Processing》 |2019年第2019期|共8页
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正文语种 eng
中图分类声学;
关键词
Minimum error entropy; Sparse system identification; Bias; Non-Gaussian;

机译：最小误差熵;稀疏系统识别;偏见;非高斯;

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专利

1. Sparse System Identification in The Presence of Noisy Input Signal Using Biased Compensator Minimum Error Entropy Algorithm [J] . WSEAS Transactions on Signal Processing . 2019,第期

机译：使用偏置补偿器最小误差熵算法在存在嘈杂输入信号时稀疏系统识别
2. Proportionate Minimum Error Entropy Algorithm for Sparse System Identification [J] . Badong Chen, Haiquan Zhao, Jose C. Principe, Entropy . 2015,第9期

机译：稀疏系统辨识的比例最小误差熵算法
3. An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals [J] . Yoo JinWoo, Shin JaeWook, Park PooGyeon Circuits and Systems II: Express Briefs, IEEE Transactions on . 2015,第3期

机译：稀疏系统中针对噪声输入信号的改进NLMS算法
4. Bias-Compensated Generalized Mixed Norm Algorithm via CIM Constraints for Sparse System Identification with Noisy Input [C] . MA Wentao, ZHENG Dongqiao, ZHANG Zhiyu Chinese Control Conference . 2017

机译：具有噪声输入的稀疏系统识别CIM约束的偏置补偿广义混合规范算法
5. Frequency Domain System Identification of Linear Time-Invariant Single-Input Single-Output 2-Dimensional Systems and Multi-Input Multi-Output 1-Dimensional Systems by Utilizing AFD Type Algorithms [D] . Wang, Xiaoyin. 2020

机译：通过利用AFD型算法，频域系统识别线性时间不变的单输入单输出二维系统和多输入多输出1维系统
6. Convergence and Stability of a Class of Iteratively Re-weighted Least Squares Algorithms for Sparse Signal Recovery in the Presence of Noise [O] . Behtash Babadi, Demba Ba, Patrick L. Purdon, -1

机译：噪声存在下一类用于稀疏信号恢复的迭代加权最小二乘算法的收敛性和稳定性
7. Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input [O] . Ma, Wentao, Zheng, Dongqiao, Li, Yuanhao, 2017

机译：用于maTLaB的偏差补偿归一化最大熵准则算法具有噪声输入的系统识别
8. Algorithms for Single-Signal and Multisignal Minimum-Cross-Entropy Spectrum Analysis [R] . Johnson, R. W. 1983

机译：单信号和多信号最小交叉熵谱分析算法

Sparse System Identification in The Presence of Noisy Input Signal Using Biased Compensator Minimum Error Entropy Algorithm

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