%0 Journal Article
%T Chaotic neural network method for job-shop scheduling problems based on improved computational energy function<br>改进计算能量函数下作业车间调度的混沌神经网络方法
%A XU Xin-li
%A WANG Wan-Hang
%A WU Qi-di
%A <br>徐新黎
%A 王万良
%A 吴启迪
%J 控制理论与应用
%D 2004
%I 
%X Neural network is an effective approach to solving job-shop scheduling problem (JSP). The paper studies the neural network method for JSP in order to obtain the global optimal solution or a feasible solution close to the optimal one. A new expression is given for the computational energy function with all constraints imposed on JSP. Moreover, applying chaos to a discrete Hopfield neural network for JSP, an improved instantaneous method of the chaotic neural network for JSP is proposed. The simulation results show that the method not only has the ability of searching for the global minimum, but also has a rather fast convergence rate. More importantly, the method guarantees the steady output of the neural network to be a feasible solution to JSP, which either itself is the global optimum or is close to the global optimum.
%K job-shop scheduling problems
%K chaos
%K neural networks
%K computational energy function<br>作业车间调度问题
%K 混沌
%K 神经网络
%K 计算能量函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=06BD4B69684C349E&yid=D0E58B75BFD8E51C&vid=659D3B06EBF534A7&iid=0B39A22176CE99FB&sid=A2745AA1110798CA&eid=AA5FB09E1F81059E&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=9