Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments

Zhao, Jinwen; Zhang, Hongbin; Zhang, J. Andrew

doi:10.1016/j.sigpro.2020.107524

Cited by 30 publications

(8 citation statements)

References 25 publications

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“…Cubic spline curves, which mainly include B-spline 25 and Catmul-Rom (CR) spline 26 . Because of the feature that CR-spline passes through all of the control points, CR-spline may has much better performance in local approximation with respect to B-spline 27 .…”

Section: Methodsmentioning

confidence: 99%

Frequency domain maximum correntropy criterion spline adaptive filtering

Guo

Zhi

Feng

2021

Sci Rep

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A filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter. However, the FDSAF algorithm is unable to suppress non-Gaussian impulsive noises. To suppression non-Gaussian impulsive noises along with having comparable operation time, a maximum correntropy criterion (MCC) based frequency domain spline adaptive filter called frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper. Further, the bound on learning rate for convergence of the proposed algorithm is also studied. And through experimental simulations verify the effectiveness of the proposed algorithm in suppressing non-Gaussian impulsive noises. Compared with the existing frequency domain spline adaptive filter, the proposed algorithm has better performance.

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Section: Methodsmentioning

confidence: 99%

Frequency domain maximum correntropy criterion spline adaptive filtering

Guo

Zhi

Feng

2021

Sci Rep

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“…Inspired by this, the generalized C-loss (GC-loss) function between the random variables X and Y can be defined as

where

means the error information. Equation ( 7 ) shows that minimizing GC-loss is equivalent to maximizing generalized correntropy, which is usually denoted as GMCC and is widely used in the field of adaptive filtering [ 18 , 20 , 25 ].…”

Section: Preliminariesmentioning

confidence: 99%

“…Although correntropy can provide a generalized similarity measure between two random variables, the Gaussian kernel function used in the MCC criterion may not always be the best choice. Therefore, as an extension of the MCC criterion, the correntropy criterion based on the generalized Gaussian density function, usually called the generalized maximum correntropy criterion (GMCC), has been proposed and widely used in the field of robust adaptive filtering [ 16 , 17 , 18 , 19 , 20 , 21 ]. Similar to the LMS algorithm family, when the correlation degree of the input signal gradually increases, the gradient-based generalized maximum correntropy criterion algorithm (GB-GMCC) will have obvious convergence performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Zhao

Qiao

et al. 2022

Entropy

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For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.

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“…Therefore, it has been widely used in filtering problems. [34][35][36] Based on the above discussion, an incipient fault prediction method based on generalized correntropy criterion filtering is proposed in this article, which is used to predict the simultaneous actuator and sensor faults in the nonlinear stochastic distribution system.…”

Section: Introductionmentioning

confidence: 99%

“…Because the generalized correntropy criterion cannot only describe the non‐Gaussian and nonlinear characteristics of random variables, but also has strong robustness because it is insensitive to outliers. Therefore, it has been widely used in filtering problems 34‐36 . Based on the above discussion, an incipient fault prediction method based on generalized correntropy criterion filtering is proposed in this article, which is used to predict the simultaneous actuator and sensor faults in the nonlinear stochastic distribution system.…”

Section: Introductionmentioning

confidence: 99%

Incipient fault prediction for nonlinear stochastic distribution systems

Zhang

Yao

2022

Intl J Robust & Nonlinear

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In order to avoid the occurrence of major accidents in the industrial production process, incipient fault prediction that can predict fault in advance has attracted more and more attentions. In this article, a generalized correntropy filtering‐based incipient fault prediction strategy is proposed, which is suitable for nonlinear stochastic distribution systems with simultaneous actuator and sensor faults and non‐Gaussian noise. Three filters are designed to predict the incipient fault of nonlinear stochastic distribution systems: the first one is used to predict the time when the incipient fault occurs, and the other two are used to estimate the evolution rate of sensor fault and actuator fault, respectively. When the residual signal exceeds the predetermined threshold, the fault is detected and the time of occurrence can be also estimated. Once the fault is detected, the designed filter can be used to estimate the evolution rate of the unknown fault. After estimating the time of fault appearance and its evolution rate, the change process of incipient fault can be predicted. Finally, an example of a chemical reaction process is given to illustrate the validity of the prediction scheme.

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Generalized maximum correntropy algorithm with affine projection for robust filtering under impulsive-noise environments

Cited by 30 publications

References 25 publications

Frequency domain maximum correntropy criterion spline adaptive filtering

Frequency domain maximum correntropy criterion spline adaptive filtering

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Incipient fault prediction for nonlinear stochastic distribution systems

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