Results 81 to 90 of about 935 (185)
Mean square cross error: performance analysis and applications in non-Gaussian signal processing
EURASIP Journal on Advances in Signal Processing, 2021 Most of the cost functions of adaptive filtering algorithms include the square error, which depends on the current error signal. When the additive noise is impulsive, we can expect that the square error will be very large.
Yunxiang Zhang +3 more
doaj +1 more source
Engineering Reports, Volume 7, Issue 1, January 2025.Simulation model of proposed system. ABSTRACT The increasing global energy demand driven by climate change, technological advancements, and population growth necessitates the development of sustainable solutions. This research investigates the design, modeling, and simulation of a 2.5 MW solar‐wind hybrid renewable energy system (SWH‐RES) optimized for
F. Max Savio +5 more
wiley +1 more source
A Robust Adaptive Filter for a Complex Hammerstein System
Entropy, 2019 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
Guobing Qian, Dan Luo, Shiyuan Wang
doaj +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
Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy
IEEE Access, 2019 Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision.
Shiwei Fan +5 more
doaj +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
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
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
Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm [PDF]
, 2016 Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both ...
Radhika, S., Sivabalan, A.
core +1 more source
IET Generation, Transmission &Distribution, Volume 19, Issue 1, January/December 2025.This paper proposes a robust, user‐type‐specific anomaly detection method for electricity usage. First, after data cleaning and preprocessing, a correntropy‐based K‐means clustering method is proposed to perform robust clustering, effectively separating users with non‐Gaussian noisy data.
Teng Zhang +4 more
wiley +1 more source