Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction - Xu, Xinghan; Ren, Weijie (ISA Transactions, 2022)
In the paper, a novel kernel recursive least-squares (KRLS) algorithm named random Fourier feature kernel recursive maximum mixture correntropy (RFF-RMMC) algorithm is proposed, which improves the prediction efficiency and robustness of the KRLS algorithm. Random Fourier feature (RFF) method as well as maximum mixture correntropy criterion (MMCC) are combined and applied into KRLS algorithm afterwards. Using RFF to approximate the kernel function in KRLS with a fixed cost can greatly reduce the computational complexity and simultaneously improve the prediction efficiency. In addition, the MMCC maintains the robustness like the maximum correntropy criterion (MCC). More importantly, it can enhance the accuracy of the similarity measurement between predicted and true values by more flexible parameter settings, and then make up for the loss of prediction accuracy caused by RFF to a certain extent. The performance of the RFF-RMMC algorithm for online time series prediction is verified by the simulation results based on three datasets. •A kernel model named RFF-RMMC is proposed for online time series prediction.•A kernel approximation method is applied to increase the calculation speed.•The accuracy of similarity is enhanced by maximum mixture correntropy criterion.•The proposed model can predict the time series with low complexity.