摘要
本文提出一类可用于模式识别的联想神经网络的综合方法,这类网络结构不受对称联接的限制,网络保证了要求的M类模式的稳定形成,且网络的容量远远超过Hopfield的联想神经网络,网络渐近稳定平衡点的吸引特性使受噪声污染的模式能得以正确恢复,体现了神经网络的非线性滤波性质。文中给出了综合一个这类联想网络计算机模拟以及模式识别的例子。
In this paper, we give a new method for synthesis of a class of associ-
ative neural networks used for pattern recognition. The associative memory networks
synthesized here is not limited by asymmetric interconnection and can guarantce to
be of M stable pattern formation desired. The capacity of the networks is over than
that of Hopfield's CAM. The properties of attraction domains of asymptotically
stable equilibria in the network, representing a nonlincar filter characteristic,
enable the pattern contaminated to be restored correctly. A network synthesized and
pattern recongnition with computer simulation have been illustrated in the paper.
出处
《电讯技术》
北大核心
1992年第1期26-29,51,共5页
Telecommunication Engineering
关键词
信息处理系统
神经网络
模式识别
Information Processing System
Neural Networks
Pattern Recongnition
