Radar target classification based on radial basis function and modified radial basis function networks

Author

Guosui, Liu ; Yunhong, Wang ; Chunling, Yang ; Dequan, Zhou

Author_Institution

Dept. of Electron. Eng., Nanjing Univ. of Sci. & Technol., China

fYear

1996

fDate

8-10 Oct 1996

Firstpage

208

Lastpage

211

Abstract

This paper discusses the radial basis function (RBF) neural networks used in the radar target classification. To enhance the classification rate, the structure of the modified radial basis function (MRBF) neural network is proposed. Two kinds of MRBF networks which are called the MRBF1 network and the MRBF2 network are discussed in this paper. From the theory as well as computer simulations, we find that the performance of the MRBF network is superior to the RBF network and the MRBF2 network gets higher classification rate than the MRBF1 network

Keywords

ART neural nets; feedforward neural nets; pattern classification; radar computing; radar target recognition; MRBF networks; MRBF1 network; MRBF2 network; RBF neural networks; adaptive resonance theory; classification rate; computer simulation; modified radial basis function networks; radar target classification; radial basis function networks; Computational modeling; Gaussian processes; Least squares approximation; Niobium; Partial response channels; Radar; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar, 1996. Proceedings., CIE International Conference of

Conference_Location

Beijing

Print_ISBN

0-7803-2914-7

Type

conf

DOI

10.1109/ICR.1996.573808

Filename

573808