Research on Online LSSVM Prediction Based on Hermite Matrix Inversion

Authors

Hong Bei, Naval Aeronautical and Astronautical University, Yantai 264001, China;
Xuepeng Jiang, Naval Aeronautical and Astronautical University, Yantai 264001, China;
Yudong Qi, Naval Aeronautical and Astronautical University, Yantai 264001, China;
Qinghua Chen, Naval Aeronautical and Astronautical University, Yantai 264001, China;

Abstract

Abstract: For forecasting the gyro drift tendency of a missile, a prediction model based on LSSVM was established taking the time series of gyro's drift as study object, and an online algorithm for nonlinear system based on hermite matrix inversion was proposed. The mathematical model of regression LSSVM, and online learning algorithm were introduced. Based on the characteristic that the reproducing kernel matrix is Hermite and positive definite, a new online learning algorithm was proposed by matrix block-inversion. The algorithm was applied to prediction research on real gyro drift data of certain missile. The experiment results show that the algorithm fully utilizes the historical training results, reduces storage space and calculate, has a fast operation speed and a high prediction precision.

Recommended Citation

Bei, Hong; Jiang, Xuepeng; Qi, Yudong; and Chen, Qinghua (2020) "Research on Online LSSVM Prediction Based on Hermite Matrix Inversion," Journal of System Simulation: Vol. 29: Iss. 1, Article 1.
DOI: 10.16182/j.issn1004731x.joss.201701001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss1/1

First Page

1

Revised Date

2014-11-12

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201701001

Last Page

6

CLC

TP277

Recommended Citation

Hong Bei, Jiang Xuepeng, Qi Yudong, Chen Qinghua. Research on Online LSSVM Prediction Based on Hermite Matrix Inversion[J]. Journal of System Simulation, 2017, 29(1): 1-6.

DOI

10.16182/j.issn1004731x.joss.201701001