一种基于MRⅡ算法的三层二值双向联想记忆网络  

A three-layer binary bidirectional associative memory network based on MRⅡ algorithm

作　　者：徐彦[1] 熊迎军[1] 

机构地区：[1]南京农业大学信息科学技术学院,江苏南京210095

出　　处：《计算机工程与科学》2018年第2期374-380,共7页Computer Engineering & Science

基　　金：国家自然科学基金(61403205);中央高校基本科研业务费专项资金(KJQN201549)

摘　　要：传统的两层二值双向联想记忆(BAM)网络因其结构的限制存在着存储容量有限、区分小差别模式和存储非正交模式能力不足的缺陷,结构上将其扩展至三层网络是一个有效的解决思路,但是三层二值BAM网络的学习是一个难题,而三层连续型BAM网络又存在处理二值问题不方便的问题。为了解决这些问题,提出一种三层结构的二值双向联想记忆网络,创新之处是采用了二值多层前向网络的MRⅡ算法实现了三层二值BAM网络的学习。实验结果表明,基于MRⅡ算法的三层二值BAM网络极大地提高了网络的存储容量和模式区分能力,同时保留了二值网络特定的优势,具有较高的理论与实用价值。Because of the structural limitation of traditional two-layer binary bidirectional associative memory（BAM）network,there are some defects such as limited storage capacity,insufficiency to distinguish small differences in patterns,and lack of capacity to store non-orthogonal patterns.It is a solution that extending it to three-layer network.However,the learning of three-layer binary BAM network is a difficult problem,and three-layer continuous BMA network is inconvenient to deal with binary problems.In order to solve these problems,a three-layer binary BAM network is proposed.The network takes advantage of the MR Ⅱlearning algorithm for multilayer binary feedforward neural networks to perform the learning.The experimental results show that the three-layer binary BAM network based on MR Ⅱ algorithm can improve the storage capability effectively and retain the advantages of binary network,so it has relative high theoretical and practical values.

关 键 词：三层二值双向联想记忆网络 双向联想记忆网络 模式存储 二值神经网络 MRⅡ算法 最小扰动原则 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]