Research on Availability of Satellite Images Based on Immune Feedback Learning Fuzzy Neural Network

Author

Zheng, Hong ; Wu, Xinghua ; Cao, Qiong

Author_Institution

Sch. of Autom. Sci. & Electr. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China

Volume

3

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

544

Lastpage

547

Abstract

In this paper, we propose a fuzzy neural network based on immune feedback learning (FNNBIFL) for the availability classifier of satellite images, which accelerates the learning speed, solves the problem of being trapped in the local minimum and improves the learning performance of fuzzy neural network. Using 122 satellite images, we compare the recognition results of the availability classifier trained by FNNBIFL or the traditional BP algorithm. Those results show that the recognition errors of FNNBIFL are reduced by 5.1%, and its learning speed is improved by 55%.

Keywords

artificial satellites; feedback; fuzzy neural nets; image classification; learning (artificial intelligence); remote sensing; BP algorithm; availability classifier; fuzzy neural network; immune feedback learning; learning speed; recognition errors; satellite images; Acceleration; Adaptive systems; Availability; Fuzzy neural networks; Immune system; Inference algorithms; Neural networks; Neurofeedback; Satellites; Stability; artificial immune; availability of satellite images; fuzzy neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.386

Filename

5359038