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%.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on availability of satellite images based on immune feedback learning fuzzy neural network


    Contributors:
    Zheng, Hong (author) / Wu, Xinghua (author) / Cao, Qiong (author)


    Publication date :

    2009


    Size :

    4 Seiten, 6 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Feature detection in satellite images using neural network technology

    Augusteijn, Marijke F. / Dimalanta, Arturo S. | NTRS | 1992


    Neural Network Based Adaptive Flight Control Using Feedback Error Learning

    Haga, Ryota / Matsuura, Akiko / Suzuki, Shinji et al. | AIAA | 2006


    Training a neural network to detect jet contrails in satellite images

    Meinert, D. | British Library Conference Proceedings | 1994


    Training a neural network to detect jet contrails in satellite images

    Meinert, D. / EUMETSAT / Meteorological Institute of Portugal | British Library Conference Proceedings | 1994