{"title":"Convergence of a gradient algorithm with penalty for training two-layer neural networks","authors":"Hongmei Shao, Lijun Liu, Gaofeng Zheng","doi":"10.1109/ICCSIT.2009.5234616","DOIUrl":null,"url":null,"abstract":"In this paper, a squared penalty term is added to the conventional error function to improve the generalization of neural networks. A weight boundedness theorem and two convergence theorems are proved for the gradient learning algorithm with penalty when it is used for training a two-layer feedforward neural network. To illustrate above theoretical findings, numerical experiments are conducted based on a linearly separable problem and simulation results are presented. The abstract goes here.","PeriodicalId":342396,"journal":{"name":"2009 2nd IEEE International Conference on Computer Science and Information Technology","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 2nd IEEE International Conference on Computer Science and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSIT.2009.5234616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper, a squared penalty term is added to the conventional error function to improve the generalization of neural networks. A weight boundedness theorem and two convergence theorems are proved for the gradient learning algorithm with penalty when it is used for training a two-layer feedforward neural network. To illustrate above theoretical findings, numerical experiments are conducted based on a linearly separable problem and simulation results are presented. The abstract goes here.