形态学联想记忆框架研究

形态学联想记忆(MAM)是一类极为新颖的人工神经网络.典型的MAM实例对象包括:实域MAM(RMAM)、复域MAM(CMAM)、双向MAM(MBAM)、模糊MAM(FMAM)、增强的FMAM(EFMAM)、模糊MBAM(FMBAM)等.它们虽有许多诱人的优点和特点,但有相同的形态学理论基础,本质上是相通的,将其统一在一个MAM框架中是可能的.同时,联想记忆统一框架的建立也是当前的研究重点和难点之一.为此作者构建了一个形态学联想记忆框架.文中首先分析MAM类的代数结构,奠定可靠的MAM框架计算基础;其次,分析MAM类的基本操作和共同特征,抽取它们的本质属性和方法,引入形态学联想记忆范式和算子;最后,提炼并证明主要的框架定理.该框架的意义在于:(1)从数学的角度将MAM对象统一在一起,从而能以更高的视角揭示它们的特性和本质;(2)有助于发现一些新的形态学联想记忆方法,从而解决更多的联想记忆、模式识别、模糊推理等问题.

Research on the Framework of Morphological Associative Memories

The morphological associative memories(MAM)are a class of extremely new artificial neural networks.Typical objects of MAM include real MAM(RMAM),complex MAM(CMAM),morphological bidirectional associative memories(MBAM),fuzzy MAM(FMAM),enhanced FMAM(EFMAM),fuzzy MBAM(FMBAM),and so on.They have many attractive advantages and features.However,they have the same morphological theoretical base in essence and it is therefore possible to unify them in a framework of MAM.At the same time,it is one of the most import...