scholarly journals KalmanTune: A Kalman Filter Based Tuning Method to Make Boosted Ensembles Robust to Class-Label Noise

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145887-145897
Author(s):  
Arjun Pakrashi ◽  
Brian Mac Namee
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108331-108342
Author(s):  
Rui Yang ◽  
Li-Yi Li ◽  
Ming-Yi Wang ◽  
Cheng-Ming Zhang ◽  
Yi-Ming Zeng-Gu

2018 ◽  
Vol 275 ◽  
pp. 2374-2383 ◽  
Author(s):  
Maryam Sabzevari ◽  
Gonzalo Martínez-Muñoz ◽  
Alberto Suárez

2017 ◽  
Vol 9 (2) ◽  
pp. 173 ◽  
Author(s):  
Charlotte Pelletier ◽  
Silvia Valero ◽  
Jordi Inglada ◽  
Nicolas Champion ◽  
Claire Marais Sicre ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Zhang ◽  
Xu-Feng Cheng

This paper concerns the parameter tuning and the estimated results postprocessing of the extended Kalman filter for the sensorless control application of permanent magnet synchronous motors. At first an extended Kalman filter parameter tuning method is proposed based on the theoretical and simulation analysis of extended Kalman filter parameters. Furthermore, a sensorless control system is proposed based on the parameter tuning method and the simulation analysis of extended Kalman filter estimation results in different reference speeds and different load torques. The proposed sensorless control system consists of two parts. The first one is a module to self-regulate extended Kalman filter parameters. The second part can correct the estimated speed and the estimated rotation angle based on the reference speed and the electromagnetic torque. Finally, simulation results are presented to verify the feasibility and validity of the proposed sensorless control system.


2021 ◽  
Author(s):  
Robert K. L. Kennedy ◽  
Justin M. Johnson ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Big Data ◽  

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