Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gra...

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Bibliographic Details
Main Authors: Nan Xue, Xiong Luo, Yang Gao, Weiping Wang, Long Wang, Chao Huang, Wenbing Zhao
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Entropy
Subjects:
kernel adaptive filtering
conjugate gradient
correntropy
sparsification criterion
malware prediction
Online Access:https://www.mdpi.com/1099-4300/21/8/785

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https://www.mdpi.com/1099-4300/21/8/785

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