Nearest Kronecker Product Decomposition Based Generalized Maximum Correntropy and Generalized Hyperbolic Secant Robust Adaptive Filters

Bhattacharjee, Sankha Subhra; Kumar, K. Suresh; George, Nithin V.

doi:10.1109/lsp.2020.3017106

Cited by 45 publications

(10 citation statements)

References 24 publications

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“…Bernoulli process with a success probability of p q , and a (n) is a zero-mean Gaussian white noise process. The MKRHS algorithm is compared with various kernel adaptive filtering algorithms such as KLMS, MKRSL, KGHSF [17], P-KLLAD [18], KMCC, and KGMC. The optimal parameter selections for each algorithm are presented in Table 1.…”

Section: Nonlinear System Identificationmentioning

confidence: 99%

“…The incorporation of the hyperbolic secant function into the adaptive filtering algorithm has demonstrated improved filter performance, including lower steady-state error, enhanced robustness, and better convergence [17].This study integrates the hyperbolic secant function with the Minimum Kernel Risk-Sensitive Loss algorithm, aiming to augment the efficacy of KAF algorithms in non-Gaussian impulsive noise settings. Concurrently, to further reduce the computational complexity of the algorithm, the vector quantization method is used to suppress the growth of its network size.…”

Section: Introductionmentioning

confidence: 99%

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A Quantized Minimum Kernel Risk Hyperbolic Secant Adaptive Filtering Algorithm

Yuanlian,

Zhiling,

Yongfeng

et al. 2023

Preprint

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The proposed algorithm in this paper is the Quantized Minimum Kernel Risk Hyperbolic Secant Adaptive Filtering Algorithm, which offers a simplified approach to enhancing the performance and stability of kernel adaptive filtering in non-Gaussian noise environments. The algorithm features a newly developed Minimum Kernel Risk Hyperbolic Secant Cost Function, which harnesses the hyperbolic secant function’s strengths to diminish outlier impacts and expedite convergence. In addition, its convex kernel risk-sensitive loss surface facilitates swift and accurate filtering via gradient-based methods , thus ensuring outlier robustness. This method could effectively manage network size and reduce computational complexity by incorporating vector quantization for inputting spatial data. Simulation tests in Mackey-Glass time series prediction and nonlinear system identification have indicated that the Minimum Kernel Hyperbolic Secant Adaptive Filtering Algorithm and its quantized variant excel in terms of convergence speed, robustness, and computational efficiency.

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Section: Nonlinear System Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Quantized Minimum Kernel Risk Hyperbolic Secant Adaptive Filtering Algorithm

Yuanlian,

Zhiling,

Yongfeng

et al. 2023

Preprint

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“…The correntropy or the logarithmic hyperbolic function is considered a candidate for the cost function in the switching algorithm for super-Gaussian noises due to their robustness [ 37 ]. In an endeavor to achieve lower steady-state misalignment, a generalized hyperbolic secant function as a robust norm and derive the generalized hyperbolic secant adaptive filter was proposed [ 38 ]. To address both Gaussian and non-Gaussian noises with a uniform expression, Liu and colleagues [ 39 ] proposed a novel HTCC algorithm by combining nonlinear function and mapping mode.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Guan

Cheng²,

Zhao

et al. 2021

PLoS ONE

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Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.

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“…The algorithms combine the logarithmic hyperbolic cosine (LHC) as cost function for nonlinear system identification 11 , 12 . Based on logarithmic hyperbolic cosine (LHC) cost function, proposed novel cost function exponential hyperbolic cosine function (EHCF) 13 , generalized hyperbolic secant 14 , formed adaptive filtering. The above SAF type algorithms are carried out in time domain.…”

Section: Introductionmentioning

confidence: 99%

“…However, the frequency domain spline adaptive filtering is derived by minimising the squared value of the instantaneous error, unable to suppress non-Gaussian impulsive noises. According to the maximum correlation entropy criterion (MCC) combined with adaptive filtering 14 , 16 – 18 , combined with spline adaptive filtering 19 – 21 , the robustness of MCC is demonstrated. To suppression non-Gaussian impulsive noises along with having comparable operation time, a frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper.…”

Section: Introductionmentioning

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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Nearest Kronecker Product Decomposition Based Generalized Maximum Correntropy and Generalized Hyperbolic Secant Robust Adaptive Filters

Cited by 45 publications

References 24 publications

A Quantized Minimum Kernel Risk Hyperbolic Secant Adaptive Filtering Algorithm

A Quantized Minimum Kernel Risk Hyperbolic Secant Adaptive Filtering Algorithm

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Frequency domain maximum correntropy criterion spline adaptive filtering

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