High Performance Associative Memory and Weight Dilution

摘要：

The consequences of diluting the weights of the standard Hopfield architecture associative memory model, trained using perceptron like learning rules, is examined. A proportion of the weights of the network are removed; this can be done in a symmetric and asymmetric way and both methods are investigated. This paper reports experimental investigations into the consequences of dilution in terms of: capacity, training times and size of basins of attraction. It is concluded that these networks maintain a reasonable performance ai fairly nigh dilution rates.

关键词： Associative Memory Hopfield Networks Weight Dilution Capacity Basins of Attraction Perceptron Learning.

作者: N.Davey R.G.Adams S.P.Hunt

作者单位: Department of Computer Science,University of Hertfordshire,College Lane, Hatfield, AL10 9AB. United Kingdom

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 635-640

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）