Title :
The Electric Field Controlling Method Based on GM (1,1) RBF Neural Network
Author :
Hao, Tan ; Lu, Xiong ; Shenguang, Gong
Author_Institution :
Dept. of Weaponry Eng., Naval Univ. of Eng., Wuhan, China
Abstract :
To avoid detection or attack, electric field signal characteristic of ships should be controlled. The signal data is decomposed into low frequency component and high frequency component first. The high frequency component is predicted by RBF neural network, the low frequency component is predicted by GM(1,1) model and add up both of the values predicted. Then countercurrent is exported and then the electric signal is weakened. Based on the data got from sea, the simulations show that 70% of shaft-rate (SR) signal amplitude would be weakened by this method, and static electric field signal is almost eliminated at all.
Keywords :
electric fields; radial basis function networks; ships; signal detection; signal processing; GM (1,1)_RBF neural network; SR signal amplitude; attack avoidance; countercurrent; detection avoidance; electric field controlling method; electric field signal characteristic; electric signal; high frequency component; low frequency component; shaft-rate signal amplitude; ships; signal data; static electric field signal; Corrosion; Electric fields; Marine vehicles; Mathematical model; Neural networks; Predictive models; Training; 1) model; Electric field; GM (1; RBF neural network; Signal character controlling; Wavelet Decomposition;
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
DOI :
10.1109/CSSS.2012.541
