Results 81 to 90 of about 1,526 (199)
Correntropy Based Divided Difference Filtering for the Positioning of Ships
Sensors, 2018 In this paper, robust first and second-order divided difference filtering algorithms based on correntropy are proposed, which not only retain the advantages of divided difference filters, but also exhibit robustness in the presence of non-Gaussian noises,
Xi Liu +3 more
doaj +1 more source
Nonlinear Component Analysis Based on Correntropy [PDF]
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006 In this paper, we propose a new nonlinear prin- cipal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal directions are found by eigen-decomposition of the correntropy matrix, which has the same dimension as the standard covariance matrix ...
Jian-Wu Xu +3 more
openaire +1 more source
IET Control Theory &Applications, Volume 19, Issue 1, January/December 2025.This work addresses the issue of rejection delay due to DoS attacks triggered by historical measurements during the transmission of a large amount of measurement data in WECS‐based networked microgrids. We propose a novel robust SRCKF method, designated as MCC‐SRCKF, which incorporates MCC into the SRCKF structure of DSE.
Xiao Hu +4 more
wiley +1 more source
Correntropy-based nonlinearity test applied to patients with chronic heart failure [PDF]
, 2010 In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively).
Sörnmo, Leif +7 more
core +1 more source
KLMS‐Net: Deep unrolling for kernel least mean square algorithm
Electronics Letters, Volume 61, Issue 1, January/December 2025.This letter proposes a novel network framework based on the deep unrolling of kernel least mean square (KLMS‐Net). KLMS‐Net transforms the iterative process of KLMS into the forward propagation of deep neural networks, which learn the implicit feature mappings in a model‐driven manner, providing deep neural networks with explicit interpretability ...
Yu Tang +5 more
wiley +1 more source
Maximum correntropy criterion based regression for multivariate calibration
, 2017 The least-squares criterion is widely used in the multivariate calibration models. Rather than using the conventional linear least-squares metric, we employ a nonlinear correntropy-based metric to describe the spectra-concentrate relations and propose a ...
Hu, Yong +9 more
core +1 more source
Robust Maximum Correntropy Kalman Filter
International Journal of Robust and Nonlinear ControlABSTRACTThe Kalman filter provides an optimal estimation for a linear system with Gaussian noise. However, when the noises are non‐Gaussian in nature, its performance deteriorates rapidly. For non‐Gaussian noises, maximum correntropy Kalman filter (MCKF) is developed which provides a more accurate result.
Joydeb Saha, Shovan Bhaumik
openaire +2 more sources
IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.This paper proposes an attention‐guided semi‐supervised model for transformer fault diagnosis using vibration‐acoustic data fusion. The model employs multilevel attention and a consistency learning strategy to enhance diagnostic accuracy under limited labelled data.
Yanfei Sun +3 more
wiley +1 more source
Novel time delay estimation algorithm for ADS-B based on an auxiliary source and correntropy
Tongxin xuebao, 2015 A novel time delay estimation algorithm was proposed to correct ADS-B latency employing an auxiliary signal source and correntropy.Firstly,the cause of message delay was analyzed and a new latency model was raised.Secondly,when comparing the signal ...
Sheng-yang LUAN +4 more
doaj
A Student's T Distribution‐Based Filter Design for SINS/GNSS With Heavy‐Tailed Noise
IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.To reduce the affection of outliers caused by heavy‐tailed noise, the noise model is constructed by the Student's T distribution rather than the Gaussian distribution, and the related probability density functions (PDF) are adaptively modelled as student's T PDFs with different DoF parameters.
Menghao Qian, Wei Chen, Ruisheng Sun
wiley +1 more source