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Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction
- Source :
- ISA Transactions. 126:370-376
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- 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.
- Subjects :
- Kernel recursive least squares
Similarity (geometry)
Computational complexity theory
Computer science
Applied Mathematics
Computer Science Applications
symbols.namesake
Fourier transform
Control and Systems Engineering
Robustness (computer science)
Feature (computer vision)
Kernel (statistics)
symbols
Electrical and Electronic Engineering
Time series
Instrumentation
Algorithm
Subjects
Details
- ISSN :
- 00190578
- Volume :
- 126
- Database :
- OpenAIRE
- Journal :
- ISA Transactions
- Accession number :
- edsair.doi.dedup.....1d99da44572900de2937116ee61b5c23