DocumentCode
2040295
Title
BP nets applied to ISAR object recognition
Author
Xingbin Gao ; Yongtan Liu
Author_Institution
Dept. of Radio Eng., Harbin Inst. of Technol., China
Volume
2
fYear
1993
fDate
19-21 Oct. 1993
Firstpage
819
Abstract
The performance of backpropagation (BP) neural classifiers for inverse synthetic aperture radar (ISAR) object recognition problems has been compared to that of a linear classifier and a nearest-neighbor classifier trained with the same data. The experimental results show that the error (misclassification) rate of the linear classifier is about twice that of the BP classifier, and the error rate of the BP classifier is about twice that of the nearest-neighbor classifier.<>
Keywords
backpropagation; errors; image recognition; neural nets; pattern recognition; synthetic aperture radar; telecommunications computing; ISAR object recognition; backpropagation neural net classifiers; error rate; inverse synthetic aperture radar; linear classifier; misclassification rate; nearest-neighbor classifier; training; Aircraft; Arthritis; Continuous wavelet transforms; Decision support systems; Feature extraction; Filters; Frequency; Object recognition; Strontium; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location
Beijing, China
Print_ISBN
0-7803-1233-3
Type
conf
DOI
10.1109/TENCON.1993.320139
Filename
320139
Link To Document