Results 101 to 110 of about 705 (186)

Generalized Maximum Correntropy Cubature Kalman Filter with Variational Bayesian for SINS/GPS Integrated Navigation Systems

Sensors
To address the degraded accuracy and poor robustness of Strapdown Inertial Navigation Systems (SINSs)/Global Positioning Systems (GPSs) integrated navigation systems under time-varying non-Gaussian measurement noises, this paper proposes a variational ...
Weisheng Ma, Bin Wei, Xi Liu
doaj   +1 more source

Robust sensor selection based on maximum correntropy criterion for ocean data reconstruction

Frontiers in Marine Science
Selecting an optimal subset of sensors that can accurately reconstruct the full state of the ocean can reduce the cost of the monitoring system and improve monitoring efficiency.
Qiannan Zhang, Huafeng Wu, Li’nian Liang, Xiaojun Mei, Jiangfeng Xian   +4 more
doaj   +1 more source

Quaternion recurrent neural network with real-time recurrent learning and maximum correntropy criterion

We develop a robust quaternion recurrent neural network (QRNN) for real-time processing of 3D and 4D data with outliers. This is achieved by combining the real-time recurrent learning (RTRL) algorithm and the maximum correntropy criterion (MCC) as a loss
Mandic, Danilo P., Bourigault, Pauline, Xu, Dongpo   +2 more
core  

Maximum Correntropy Ensemble Kalman Filter

, 2023
In this article, a robust ensemble Kalman filter (EnKF) called MC-EnKF is proposed for nonlinear state-space model to deal with filtering problems with non-Gaussian observation noises.
Tao, Yangtianze, Kang, Jiayi, Yau, Stephen Shing-Toung   +2 more
core  

Maximum Correntropy Unscented Filter

, 2017
International audienceThe unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather ...
Xu, Bin, Wu, Zongze, Liu, Xi, Honeine, Paul, Chen, Badong   +4 more
core  

On accuracy of PDF divergence estimators and their applicability to representative data sampling

, 2011
Generalisation error estimation is an important issue in machine learning. Cross-validation traditionally used for this purpose requires building multiple models and repeating the whole procedure many times in order to produce reliable error estimates ...
Musial, Katarzyna, Gabrys, Bogdan, Katarzyna Musial, Bogdan Gabrys, Musial-Gabrys, Katarzyna, Budka, Marcin, Marcin Budka   +6 more
core   +1 more source

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion. [PDF]

Entropy (Basel), 2019
Duan J, Tian X, Ma W, Qiu X, Wang P, An L.   +5 more
europepmc   +1 more source

Variable Bayesian-Based Maximum Correntropy Criterion Cubature Kalman Filter with Application to Target Tracking. [PDF]

Entropy (Basel)
Ma Y, Zhang G, Ye S, An D.
europepmc   +1 more source

Maximum correentropy-based robust Square-root Cubature Kalman Filter for vehicular cooperative navigation. [PDF]

Sci Rep, 2023
Sun W, Zhang X, Ding W, Zhang H, Liu A.
europepmc   +1 more source

Robust deep network with maximum correntropy criterion for seizure detection. [PDF]

Biomed Res Int, 2014
Qi Y, Wang Y, Zhang J, Zhu J, Zheng X.
europepmc   +1 more source