Radar Emitter Signal Recognition Based on Rough Set Theory
EW Lab · Xi'an Jiaotong Daxue xuebao · 2005
Rough set theory (RST) was introduced into radar emitter signal recognition. A novel approach was proposed to discretize interval-valued continuous attributes, and the corresponding feature selection method was presented. Rough set neural network (RNN) classifier was designed by combining RST and neural network (NN). Experimental results show that the proposed approach solves the problem of interval-valued continuous attribute discretization existing methods are unable to deal with, and achieves higher 7.29%, 4.34% and 4.00% recognition rate than that of the other methods. The average training generations of RNN are 97.54 less than that of NN and the average recognition rate of RNN is higher 2.84% than that of NN, which indicates that RNN has stronger capabilities of classification and generalization than NN to be expectantly applied to the practice.