对数-指数形态学联想记忆  

Logarithmic and Exponential Morphological Associative Memories

作　　者：冯乃勤[1,2] 田勇[3] 王鲜芳[1] 宋黎明[1] 范海菊[1] 王双喜[4] 

机构地区：[1]河南师范大学计算机与信息工程学院,河南新乡453007 [2]郑州工业应用技术学院信息工程学院,河南郑州451100 [3]河南机电职业学院信息工程系,河南郑州451191 [4]商丘学院计算机科学与技术学院,河南商丘476000

出　　处：《软件学报》2015年第7期1662-1674,共13页Journal of Software

基　　金：国家自然科学基金(61173071);河南省高校创新人才支持计划(2012HASTIT011)

摘　　要：利用对数和指数算子构建了一种新的形态学联想记忆方法,简称LEMAM.理论分析表明:自联想LEMAM(简称ALEMAM)具有无限存储能力、一步回忆记忆、一定的抵抗腐蚀噪声或膨胀噪声的能力,在输入完全或在一定的噪声范围内,能够保证完全回忆记忆;异联想LEMAM(简称HLEMAM)在输入完全情况下,不能保证完全回忆记忆,但当满足一定条件时,也能够达到完美联想记忆.对比实验结果表明:在一些情况下,LEMAM能够取得较好的联想记忆效果.总体来说,LEMAM丰富了形态学联想记忆的理论和实践,可以作为一种神经计算模型加以研究和利用.A novel morphological associative memory method, abbreviated as LEMAM, is constructed by using logarithmic operator and exponential operator. The theoretical analysis shows that auto LEMAM （abbreviated as ALEMAM）, which has unlimited storage capacity, one step recall, and a certain ability of resisting erosive noise or dilative noise, can ensure perfect recall memory for either perfect inputs or a certain range of noise. Hereto LEMAM （abbreviated as HLEMAM） does not guarantee perfect recall, even without any input noise. However, when meeting certain conditions, HLEMAM can also achieve perfect recall. HLEMAM contrast experiments show that, in some cases, LEMAM can produce better result. On balance, LEMAM enriches the theory and practice of morphological associative memories, and can serve as a kind of new neural computational model for research and application.

关 键 词：对数 指数 形态学联想记忆 自联想记忆 异联想记忆 完全回忆记忆 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]