基于小世界网络的Hopfield联想记忆模型

Research on the hopfield associative memory model based on the small-world network

针对基于Watts-Strogatz小世界网络的联想记忆(WSAM)模型中存在的信息丢失和产生孤立节点的问题,引入Newman-Watts小世界网络,提出了基于NW小世界网络的联想记忆(NWAM)模型,并给出生成方法以及相应的理论算法。与WSAM相比,该模型节点连接数有少量增加,而网络性能却得到极大的改善。对比实验结果表明,在重连概率和全局连接度相同的情况下,NWAM对加噪模式回想的能力要高于WSAM;在噪音干扰不断增加的情况下,NWAM抗噪联想性能始终优于WSAM。最终,利用NWAM模型对加入噪音的交通图像进行识别时,获得了比WSAM更好的识别效果,表现出良好的容错性和对含噪信息的鲁棒处理能力。

In view of the various problems associated with information loss and isolated points in the WSAM （associ- ative memory network based on the Watts-Strogatz small-world neural network）, the Newman-Watts small-world network has been introduced, and a model of the NW AM （ associative memory based on the Newman-Watts small- world neural network） is presented in this paper. This paper analyzes the NWAM and details the generation meth- ods and algorithm. The network performance has been greatly improved by increasing the number of node connec- tions. The experimental results show that under the same probability and connection degree, the NWAM delivers better performance than the WSAM. With the increase of noise interference, the noise immunity performance of the NWAM is always better than the WSAM. Finally, The NWAM is used in the traffic image recognition and the re- suits show that it is more robust and has high fault tolerance ability when compared with the WSAM.