Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
Maximum correntropy Kalman filter
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error
(MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals …
(MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals …
Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems
X Liu, H Qu, J Zhao, P Yue - ISA transactions, 2018 - Elsevier
For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are
useful methods to solve the state estimation problems, and both can obtain good …
useful methods to solve the state estimation problems, and both can obtain good …
Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking
Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on
a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the …
a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the …
Linear and nonlinear regression-based maximum correntropy extended Kalman filtering
The extended Kalman filter (EKF) is a method extensively applied in many areas,
particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is …
particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is …
Maximum correntropy unscented filter
The 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 …
problem for a non-linear dynamic system, utilising a derivative-free higher-order …
Robust regression using support vector regressions
Noisy data and outliers has always been one of the main challenges in regression
applications. The presence of these data among training data will produce several negative …
applications. The presence of these data among training data will produce several negative …
Diffusion maximum correntropy criterion algorithms for robust distributed estimation
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion
(MCC), including adapt then combine MCC and combine then adapt MCC, are developed to …
(MCC), including adapt then combine MCC and combine then adapt MCC, are developed to …
Extended least squares support vector machine with applications to fault diagnosis of aircraft engine
YP Zhao, JJ Wang, XY Li, GJ Peng, Z Yang - ISA transactions, 2020 - Elsevier
Recently, a robust least squares support vector machine (R-LSSVM) was proposed, but its
computational complexity is very high compared with the traditional least squares support …
computational complexity is very high compared with the traditional least squares support …
Nonlinear fast modeling method of flux linkage and torque for a 12/8 switched reluctance motors
X Sun, N Wang, Y Cao, D Guo, M Yao… - … on Power Electronics, 2024 - ieeexplore.ieee.org
This article presents a nonlinear modeling method for the flux linkage and torque of a
switched reluctance motor. The method is based on universal weighted least squares …
switched reluctance motor. The method is based on universal weighted least squares …