联想记忆神经网络的一个有效学习算法
An Efficient Learning Algorith for Associative Memory Neural Network
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摘要: 提出一种新的联想记忆网络模型的有效学习算法,它具有下述特点:(1)可以全部存 储任意给定的训练模式集,即对于训练模式的数目和它们之间相关性的强弱没有限制;(2)最 小的训练模型吸引域达到最大;(3)在(2)的基础上,每个训练模式具有尽可能大的吸引域; (4)联想记忆神经网络是全局稳定的.大量的计算机仿真实验结果充分说明所提出的学习算 法比已有算法具有更强的存储能力和联想容错能力.Abstract: A new and efficient learning algorithm of asociative memory neural network is proposed, with the following characteristics: (1)it can store any given training pattern set no matter how much and what correlation among them may be; (2)the smallest domain of attraction of training patterns is maximized; (3)each domain of attraction of training patterns is guaranteed to be as large as possible; (4)the designed associative memory network is globally stable. A large number of computer experimental results confirm that our algorithm possesses more powerful storage ability and more fault-tolerance capability than existing ones.
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