Results 131 to 140 of about 50,949 (199)

Extended Kalman filter under maximum correntropy criterion

2016 International Joint Conference on Neural Networks (IJCNN), 2016
As a nonlinear extension of Kalman filter, the extended Kalman filter (EKF) is also based on the minimum mean square error (MMSE) criterion. In general, the EKF performs well in Gaussian noises. But its performance may deteriorate substantially when the system is disturbed by heavy-tailed impulsive noises.
Xi Liu 0006, Hua Qu, Ji-hong Zhao 0001, Badong Chen   +3 more
openaire   +1 more source

Robust variable kernel width for maximum correntropy criterion algorithm

Signal Processing, 2021
Abstract Maximum correntropy criterion (MCC) has been widely adopted for parameter estimation in the environment of non-Gaussian noise due to its robust characteristics to non-Gaussian noises. However, choosing a proper fixed value of kernel width in MCC algorithm is not an easy task.
Wei Huang 0015, Haojie Shan, Jinshan Xu, Xinwei Yao 0001   +3 more
openaire   +1 more source

Random Fourier Filters Under Maximum Correntropy Criterion

IEEE Transactions on Circuits and Systems I: Regular Papers, 2018
Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional random Fourier feature space (RFFS) such that the network structure of filters is fixed while achieving similar performance with kernel adaptive filters. The commonly used error criterion in RFAFs is the well-known minimum mean-square error (MMSE) criterion, which
Shiyuan Wang, Lujuan Dang, Badong Chen, Shukai Duan 0001, Lidan Wang 0001, Chi K. Tse   +5 more
openaire   +1 more source

Gaussian-Cauchy Mixture Kernel Function Based Maximum Correntropy Criterion Kalman Filter for Linear Non-Gaussian Systems

IEEE Transactions on Signal Processing
This paper proposes a Gaussian-Cauchy mixture maximum correntropy criterion Kalman filter algorithm (GCM_MCCKF) for robust state estimation in linear systems under non-Gaussian noise, particularly heavy-tailed noise.
Quanbo Ge, Xuefei Bai, Pingliang Zeng
semanticscholar   +1 more source

A Privacy-Preserving Semisupervised Algorithm Under Maximum Correntropy Criterion

IEEE Transactions on Neural Networks and Learning Systems, 2022
Existing semisupervised learning approaches generally focus on the single-agent (centralized) setting, and hence, there is the risk of privacy leakage during joint data processing. At the same time, using the mean square error criterion in such approaches does not allow one to efficiently deal with problems involving non-Gaussian distribution. Thus, in
Ling Zuo, Yinghan Xu, Chi Cheng 0003, Kim-Kwang Raymond Choo   +3 more
openaire   +2 more sources

Gauss Hermite Fourier Features Based on Maximum Correntropy Criterion for Adaptive Filtering

IEEE Transactions on Circuits and Systems Part 1: Regular Papers
Kernel adaptive filters (KAFs) are a class of nonlinear adaptive filters developed in the reproducing kernel Hilbert space, and are particularly suitable for addressing signal processing issues involving data streams and unknown nonlinearities.
Tian Zhou, Shiyuan Wang, J. Qian, Yunfei Zheng, Qiangqiang Zhang, Yingying Xiao   +5 more
semanticscholar   +1 more source

Robust State Estimation of Integrated Electric-Heat System Based on Physics-Guided Deep Learning With Maximum Correntropy Criterion

IEEE Transactions on Instrumentation and Measurement
Designing an accurate and robust state estimation (SE) framework for integrated electric-heat systems (IEHSs) in the presence of non-Gaussian noise (or outliers) is a crucial challenge.
Wentao Ma, Qinxin Yan, Peng Guo, Badong Chen   +3 more
semanticscholar   +1 more source

Performance evaluation of the maximum correntropy criterion in identification systems

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 2016
The System identification explores ways to obtain mathematical models of an unknown system. However, as a result from the intrinsic random nature of system or from the environment noise, it is very hard to find a perfect mathematical representation of a real system.
João P. F. Guimarães, Aluisio I. Rêgo Fontes, Joilson B. A. Rego, Luiz Felipe Q. Silveira, Allan de M. Martins   +4 more
openaire   +1 more source

Statistical Analysis of Maximum Correntropy Criterion Subband Adaptive Filtering Algorithm

IEEE Signal Processing Letters
The correntropy contains all the even-order information of data and is well suited for dealing with non-Gaussian noise. The subband adaptive filtering algorithm combined with the maximum correntropy criterion (MCC-SAF) has been proposed to efficiently ...
Wenjing Xu, Haiquan Zhao, Shaohui Lv
semanticscholar   +1 more source

Adaptive robust cubature Kalman filter with maximum correntropy criterion and variational Bayesian for vehicle state estimation

Measurement science and technology
The Kalman filter (KF) can accurately estimate vehicle states under the assumption of Gaussian noise. However, when the measurement system is subject to complex non-Gaussian noise disturbances, the estimation performance of traditional methods degrades ...
Chaoxu Yang, Farong Kou, Wenhua Lv, Guohong Wang, Pengtao Liu, Longlong Xing   +5 more
semanticscholar   +1 more source