{"title":"A dynamic approach to improve sparsely encoded associative memory capability","authors":"Y.-P. Huang, D. Gustafson","doi":"10.1109/IJCNN.1992.287169","DOIUrl":null,"url":null,"abstract":"A method for improving sparsely encoded associative memory storage capacity based on dynamic thresholding is presented. Under the dynamic thresholding scheme, the sparsely encoding method is shown to have greater storage capacity than the ordinary associative memory. The results are also considered from the storage sensitivity point of view. Simulation results are consistent with the quantitative analysis. It is found that system capacity is strongly dependent on the selected threshold. Selection of threshold is based on each neuron working close to its threshold assumption. This makes it possible to find a more reasonable storage capacity by using signal part and mean noise only.<<ETX>>","PeriodicalId":286849,"journal":{"name":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","volume":"117 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings 1992] IJCNN International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1992.287169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A method for improving sparsely encoded associative memory storage capacity based on dynamic thresholding is presented. Under the dynamic thresholding scheme, the sparsely encoding method is shown to have greater storage capacity than the ordinary associative memory. The results are also considered from the storage sensitivity point of view. Simulation results are consistent with the quantitative analysis. It is found that system capacity is strongly dependent on the selected threshold. Selection of threshold is based on each neuron working close to its threshold assumption. This makes it possible to find a more reasonable storage capacity by using signal part and mean noise only.<>
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