%0 Journal Article
%T A Generalized Updating Rules Using Hopfield-Type Neural Networks for Optimization Problems<br>Hopfield-型网络求解优化问题的一般演化规则
%A QIU Shen-Shan
%A DENG Fei-Qi
%A LIU Yong-Qing
%A <br>邱深山
%A 邓飞其
%A 刘永清
%J 自动化学报
%D 2004
%I 
%X This paper presents two generalized updating rules based on Hopfield-type neural networks (with delay or without delay) for optimization problems. These rules are characterized by dynamic thresholds of the updating sequence. Convergence theo-rems of discrete Hopfield-type neural networks with delay are obtained, which extend the exsiting convergence results. Also obtained is a sufficient and necessary condition for the relation between the stable states of neural networks and the points of local maximum value of energy function. Decomposed strategy is given in order to apply the Hopfield-type neural networks with delay to optimization problems effectively. Finally, the experimental results demonstrate that the given algorithm improves the convergence rate and decreases the updating time when compared with Hopfield-type neural network without delay.
%K Discrete Hopfield-type neural network
%K delay
%K convergence
%K stable state<br>离散Hopfield-型网络
%K 延迟
%K 收敛性
%K 稳定态
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=E7FBF1950D0CD434&yid=D0E58B75BFD8E51C&vid=340AC2BF8E7AB4FD&iid=E158A972A605785F&sid=B7B25E832E7F23D8&eid=F204392B3B11C3BD&journal_id=0254-4156&journal_name=自动化学报&referenced_num=1&reference_num=13