Aircraft image recognition using back-propagation

Author

Somaie, A.A. ; Badr, A. ; Salah, T.

Author_Institution

R&D Center, Ain Shams Univ., Cairo, Egypt

fYear

2001

fDate

2001

Firstpage

498

Lastpage

501

Abstract

The back-propagation neural network (BNN) is presented to recognize the aircraft images. Different training algorithms were used with different number of hidden neurons. The presented training algorithms satisfied the high recognition performance when the number of hidden neurons was less than or equal to ten times the number of classes. The effect of the activation function on the recognition performance and error convergence were studied and it was concluded that the log sigmoid function is better than the tanch sigmoid function. It was found that the network recognizes the test images correctly even the noisy and the incomplete images

Keywords

Gaussian noise; aircraft; backpropagation; convergence of numerical methods; error analysis; image recognition; neural nets; Gaussian noise; activation function; aircraft image recognition; back-propagation neural network; error convergence; hidden neurons; incomplete images; log sigmoid function; noisy images; recognition performance; tanch sigmoid function; test images; training algorithms; Aircraft; Convergence; Electronic mail; Image processing; Image recognition; Neural networks; Neurons; Pattern recognition; Shape; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar, 2001 CIE International Conference on, Proceedings

Conference_Location

Beijing

Print_ISBN

0-7803-7000-7

Type

conf

DOI

10.1109/ICR.2001.984755

Filename

984755