Active impulsive noise control using maximum correntropy with adaptive kernel size

Lu, Lu; Zhao, Haiquan

doi:10.1016/j.ymssp.2016.10.020

Cited by 69 publications

(38 citation statements)

References 26 publications

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“…The main property of correntropy is that it provides an effective mechanism to mitigate the influence of large outliers. Recently, correntropy has been successfully applied in various areas, such as signal processing [23], machine learning [24][25][26], adaptive filtering [27][28][29], and others [30][31][32].…”

Section: Mixture Correntropymentioning

confidence: 99%

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Xue

Luo

Gao

et al. 2019

Entropy

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Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gradient (KCG) algorithm has been proposed with low computational complexity, while achieving comparable performance to some KAF algorithms, e.g., the kernel recursive least squares (KRLS). However, the robust learning performance is unsatisfactory, when using KCG. Meanwhile, correntropy as a local similarity measure defined in kernel space, can address large outliers in robust signal processing. On the basis of correntropy, the mixture correntropy is developed, which uses the mixture of two Gaussian functions as a kernel function to further improve the learning performance. Accordingly, this article proposes a novel KCG algorithm, named the kernel mixture correntropy conjugate gradient (KMCCG), with the help of the mixture correntropy criterion (MCC). The proposed algorithm has less computational complexity and can achieve better performance in non-Gaussian noise environments. To further control the growing radial basis function (RBF) network in this algorithm, we also use a simple sparsification criterion based on the angle between elements in the reproducing kernel Hilbert space (RKHS). The prediction simulation results on a synthetic chaotic time series and a real benchmark dataset show that the proposed algorithm can achieve better computational performance. In addition, the proposed algorithm is also successfully applied to the practical tasks of malware prediction in the field of malware analysis. The results demonstrate that our proposed algorithm not only has a short training time, but also can achieve high prediction accuracy.

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Section: Mixture Correntropymentioning

confidence: 99%

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Xue

Luo

Gao

et al. 2019

Entropy

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“…Applying the above operation for (35), we can easily achieve 1 1 1 1 (31) and (36). Then, the focus is to calculate several expectations in (36), including…”

Section: B Mean Square Behavior Modelmentioning

confidence: 99%

Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis

Shi

Zhao

2018

IEEE Access

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Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in this paper, which adds an 1 -norm l penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS (LRZA-DLMS) algorithm is also put forward, which can improve the estimation performance in the presence of time-varying sparsity. Instead of using the 1 -norm l penalty, in the reweighted version, a log-sum function is employed as the substitution. Based on the weight error variance relation and several common assumptions, we analyze the transient behavior of our findings and determine the stability bound of the step-size. Moreover, we implement the steady state theoretical analysis for the proposed algorithms. Simulations in the context of distributed network system identification illustrate that the proposed schemes outperform various existing algorithms and validate the accuracy of the theoretical results.INDEX TERMS leaky, low SNR, zero attracting, sparse system, weight error variance VOLUME XX, 2017

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“…Recently, the information entropy (IE) method was applied to the active noise control system for the impulsive noise and good results were achieved [17][18][19][20] . Correntropy of IE is a nonlinear and local similarity measure between two arbitrary scalar random variables.…”

Section: Introductionmentioning

confidence: 99%

“…developed a correntropy based algorithm, which improves the robustness of NANC system for non-Gaussian reference noise signals [19]. In 2017, Lu et al proposed a filtered-x recursive maximum correntropy (FxRMC) algorithm based on MCC to reduce the effect of impulsive noise [18].…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for nonlinear active noise control of impulsive noise

2019

Applied Acoustics

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Nonlinear active noise control (NANC) systems employing Volterra filter suffer from the stability issues in the presence of impulsive noise. To solve this problem, we combine the second-order Volterra (SOV) filter and maximum correntropy criterion (MCC) in this paper. The Volterra filter-x maximum correntropy criterion (VFxMCC) algorithm and Volterra filter-x recursive maximum correntropy (VFxRMC) algorithm are applied to reduce the impulsive noise of NANC. We find that VFxMCC algorithm has a low computational complexity and VFxRMC algorithm converges fast. In order to extract their advantages, we further propose a hybrid algorithm based on the VFxMCC and VFxRMC algorithms. In addition, the normalize step-size version of VFxMCC (VFxnMCC) algorithm is developed to improve the robustness and performance. Meanwhile, we adaptively adjust the kernel size of MCC online based on the sample variance of reference signal to improve the performance of the proposed algorithms. Simulation results in the context of nonlinear active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

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Active impulsive noise control using maximum correntropy with adaptive kernel size

Cited by 69 publications

References 26 publications

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis

An efficient algorithm for nonlinear active noise control of impulsive noise

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