Previous research has mentioned that the Constrained Energy Minimization (CEM) algorithm is effective in dealing with small target detection in hyperspectral remote sensing images. The challenging work in CEM is to solve the inherent complex and large scale quadratic programming (QP) problems. However, using the traditional numerical algorithms to solve QP problems is non-significant. To this end, a novel CEM algorithm based on special zeroing neural network (termed SZCEM) model is formulated to solve this problem. Then, the feasibility and preponderance of the formulated method are proved by mathematical analysis and numerical experiments. The significance of this paper lies in expanding the application range of the ZNN model and its variants. At the same time, this paper also enriches researchers’ tools to deal with remote sensing image processing issues.


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    Titel :

    A ZNN Based Constrained Energy Minimization Algorithm and Its Application


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Yu, Xiang (Herausgeber:in) / Jiang, Chengze (Autor:in) / Xiao, Xiuchun (Autor:in) / Liu, Dazhao (Autor:in) / Huang, Haoen (Autor:in)


    Erscheinungsdatum :

    2021-10-30


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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