Generalized Complex Correntropy: Application to Adaptive Filtering of Complex Data

Qian, Guobing; Wang, Shiyuan

doi:10.1109/access.2018.2821141

Cited by 37 publications

(18 citation statements)

References 15 publications

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“…The MCC contains higher moments of probability density function and is suitable for any noise environment. So far, many robust learning algorithms have been developed using MCC [12][13][14][15][16][17][18]. It has been proved that they can perform a robust analysis [19][20][21][22] and effectively deal with non-Gaussian situations and outliers [23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

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Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

Duan

Tian

et al. 2019

Entropy

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The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users’ EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

“…That is to say, maximizing J(ω, b) with respect to [ω, b] is equivalent to maximizing the augmented function J(ω, b, p) in the enlarged parameter space [ω, b, p]. Now, to overcome the second problem, we introduce a slack vector to convert the unconstrained optimization problem (16) to the following constrained problem…”

Section: Of 18mentioning

confidence: 99%

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

Duan

Tian

et al. 2019

Entropy

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“…They cannot be directly employed to handle the complex-valued data. In fact, many signals are defined in the complex domain in practical applications [ 22 , 23 , 24 , 25 ]. Thus, in this work, we put forward a Hammerstein maximum complex correntropy criterion (HMCCC) algorithm, which extends the Hammerstein adaptive filter, using MCC criterion, to the complex domain.…”

Section: Introductionmentioning

confidence: 99%

A Robust Adaptive Filter for a Complex Hammerstein System

Qian

Luo

Wang

2019

Entropy

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The Hammerstein adaptive filter using maximum correntropy criterion (MCC) has been shown to be more robust to outliers than the ones using the traditional mean square error (MSE) criterion. As there is no report on the robust Hammerstein adaptive filters in the complex domain, in this paper, we develop the robust Hammerstein adaptive filter under MCC to the complex domain, and propose the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm. Thus, the new Hammerstein adaptive filter can be used to directly handle the complex-valued data. Additionally, we analyze the stability and steady-state mean square performance of HMCCC. Simulations illustrate that the proposed HMCCC algorithm is convergent in the impulsive noise environment, and achieves a higher accuracy and faster convergence speed than the Hammerstein complex least mean square (HCLMS) algorithm.

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“…To find out the solution for handling these problems, the maximum correntropy criterion (MCC) was proposed to give resistance to impulse-noise [36,37]. Then, a series of AF algorithms based on MCC were proposed to resist impulse-noise [38][39][40][41][42][43][44][45][46][47][48]. The variable kernel width MCC (VKW-MCC) uses a variable kernel width technique to enhance the identification ability of the famed MCC [49].…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

Jiang

Huang

et al. 2019

Symmetry

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Generalized Complex Correntropy: Application to Adaptive Filtering of Complex Data

Cited by 37 publications

References 15 publications

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

A Robust Adaptive Filter for a Complex Hammerstein System

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

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