Title :
Spectrum prediction for high-frequency radar based on Extreme Learning Machine
Author :
Zhifen Yang ; Ling Yang ; Yanping Fu
Author_Institution :
Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China
Abstract :
In this paper, a new predictive method based on Extreme Learning Machine is proposed to predict the spectrum data obtained from by frequency monitoring system of high-frequency radar. In order to improve the forecasting accuracy and real-time of spectrum prediction of high-frequency radar, Empirical Mode Decomposition method is used for the preprocessing of spectrum data. Based on the simulation environment of MATLAB, compared with the predictive method based on support vector regression, the results show that the proposed method performs better both at forecasting accuracy and speed.
Keywords :
learning (artificial intelligence); radar computing; radar signal processing; regression analysis; MATLAB; empirical mode decomposition method; extreme learning machine; forecasting accuracy; forecasting speed; frequency monitoring system; high-frequency radar; predictive method; simulation environment; spectrum data; spectrum prediction; support vector regression; Backscatter; Forecasting; IP networks; MATLAB; Nickel; Training;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2015 Seventh International Conference on
Conference_Location :
Wuyi
Print_ISBN :
978-1-4799-7257-9
DOI :
10.1109/ICACI.2015.7184784