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Wentao Ma
Researcher at Ningxia University
Publications - 111
Citations - 1956
Wentao Ma is an academic researcher from Ningxia University. The author has contributed to research in topics: Mean squared error & Computer science. The author has an hindex of 16, co-authored 96 publications receiving 1208 citations. Previous affiliations of Wentao Ma include Wuhan University & Xi'an Jiaotong University.
Papers
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Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
TL;DR: A robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum Correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation is proposed.
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Kernel recursive maximum correntropy
TL;DR: A robust kernel adaptive algorithm is derived in kernel space and under the maximum correntropy criterion (MCC), which is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive noises.
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Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
TL;DR: In this paper, the authors proposed a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum Correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation.
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Diffusion maximum correntropy criterion algorithms for robust distributed estimation
TL;DR: Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments.
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Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network
TL;DR: A robust short-term wind power hybrid forecasting model based on Long Short-term Memory neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed, and results show that proposed method is more effective than other traditional methods.