{"title":"A Study on Backpropagation in Artificial Neural Networks","authors":"C. Sekhar, P. Meghana","doi":"10.21742/AJNNIA.2020.4.1.03","DOIUrl":null,"url":null,"abstract":"Innovation assumes essential job nowadays in human life to limit the manual work. Execution and exactness with innovation will be high. The Backpropagation neural framework is multilayered, feedforward neural framework and is by a full edge the most extensively utilized. It is moreover seen as one of the least demanding and most wide systems used for managed planning of multilayered neural systems. Backpropagation works by approximating the non-direct association between the data and the yield by changing the weight regards inside. It can furthermore be summarized for the data that is rejected from the planning structures (perceptive limits).","PeriodicalId":335163,"journal":{"name":"Asia-Pacific Journal of Neural Networks and Its Applications","volume":"59 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia-Pacific Journal of Neural Networks and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21742/AJNNIA.2020.4.1.03","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
Innovation assumes essential job nowadays in human life to limit the manual work. Execution and exactness with innovation will be high. The Backpropagation neural framework is multilayered, feedforward neural framework and is by a full edge the most extensively utilized. It is moreover seen as one of the least demanding and most wide systems used for managed planning of multilayered neural systems. Backpropagation works by approximating the non-direct association between the data and the yield by changing the weight regards inside. It can furthermore be summarized for the data that is rejected from the planning structures (perceptive limits).
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
