%0 Journal Article
%T 求解具有约束的l1-范数问题的神经网络模型<br>A neural network for solving l1-norm problems with constraints
%A 李翠平
%A 高兴宝&lt
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%A LI Cui-ping
%A GAO Xing-bao
%J 山东大学学报（理学版）
%D 2018
%R 10.6040/j.issn.1671-9352.0.2017.627
%X 摘要： 提出了一个解约束最小 l1-范数问题的单层神经网络模型。与已有神经网络模型相比,提出的模型所需神经元数少且层数少。通过引入 Lyapunov 函数,证明了该模型的稳定性和收敛性。数值试验结果表明所提出的模型具有良好的性能。<br>Abstract: This paper presents a one-layer neural network model for solving l1-norm problems with constraints. Compared with some existing neural network models, the proposed model needs fewer neurons and has a simpler structure. The stability and convergence of the proposed model are proved by introducing a Lyapunov function. Some simulation examples are used to illustrate its validity and transient behaviors
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%K -范数问题
%K 神经网络
%K 单层
%K 稳定性
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%K -norm problem
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%K one-layer
%K stability
%U http://lxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1671-9352.0.2017.627