摘要
本文综合讨论了Hopfield-型神经网络的迭代算法,使其能保证能量函数单调下降;指出了文[12]中的错误,并给出了原因;解决了用离散Hopfield-型二元神经网络不能求解带有负反馈的问题的难题,大大改进了文[12]中的算法,能够成功地对任意多个国家的地图用四种颜色着色(使得任意相邻的两个国家着不同的颜色),并可对任意k-可着色问题进行求解.
In this paper, we discussed the iteration algorithm of Hopfield-type Neural Network with which the Energy Function is always guaranteed to monotonically decrease; Pointed out the error in the proof and the drawbacks of the algorithm in cite [12], and analysed the reason; Settled the difficult problem of Neural Networks for solving optimization problem with negative feedback, impproved the algorithm in cite [12]; using our method, we can successfully dye 4 different colors for every map such that any adjecnt countries dying different colors; moreover , we can solving K-colorability problem respectivly.
出处
《运筹学学报》
CSCD
1999年第3期35-43,共9页
Operations Research Transactions
基金
国家自然科学基金!(39830070)
中国科学院管理
决策与信息系统开放实验室资助