本文讨论了一类联想神经网络在学习过程中结构变化引起网络平衡点状态变化的动态特性； 研究了网络的指数稳定性质；
The exponential stability and trajectory bounds of the motions of equilibria of an associative neural network under structural variations while learning a new pattern are investigated.
在上述工作的基础上，本文主要研究了基于小世界体系的指数核自联想记忆模型在人脸识别中的应用。
On the basis of aforesaid work, the author further proposes robust face recognition algorithms based on sparse kernel auto - associative memory models.
多值指数关联联想记忆模型（MMECAM）是一种高存储容量的自联想记忆神经网络。
Modified Multi-valued Exponential Correlation Associative Memory Model (MMECAM) is a neural network with higher storage capacity.
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