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机构地区:[1]郑州工业应用技术学院信息工程学院,郑州451100 [2]河南师范大学计算机与信息工程学院,河南新乡453007 [3]河南机电职业学院信息工程系,郑州451191
出 处:《小型微型计算机系统》2015年第10期2374-2378,共5页Journal of Chinese Computer Systems
基 金:国家自然科学基金项目(61173071)资助
摘 要:形态学联想记忆是一类新颖的人工神经网络.尽管它有许多优点,然而也有一个致命的弱点,即异联想记忆效果较差.通过对形态学联想记忆框架的分析,发展了一种新的形态学联想记忆方法——加减形态学联想记忆(MAMFATS).理论分析和实验证实,该方法对于异联想形态学记忆效果显著,在一定程度上可弥补原形态学联想记忆方法的不足.新旧两类方法之间的关系是互补,而非排斥.两种方法结合起来,可以更好应对异联想形态学记忆不完全回忆记忆的困难.MAMFATS方法充实了形态学联想记忆理论,也增大了异联想形态学记忆的实用性.Morphological associative memories are a kind of novel artificial neural networks. Although they have many advantages, but also have a fatal weakness,namely the effect of betero-associative memories is poorer. Through analyzing the framework of morphological associative memories, the authors present a new morphological associative mnemonic method morphological associative memories from addition to subtraction. The theoretical analysis and experiments show that the new method for hetero-associative phological memories is significantly effective. It can make up for the original morphological associative memory to some extent. The relationship between the new and old methods is complementary, not exclusionary each other. In order to solve the difficult problem which hetero-associative morphological memories recall incompletely, two kinds of methods should be combined. The new method enriches the theory of morphological associative memories, at the same time, also increases the practical value of morphological associative memories.
分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]
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