{"title":"Optimization of parameters of neural networks by genetic algorithm in the control systems of electromechanical objects","authors":"M. P. Belov, O. Zolotov","doi":"10.1109/SCM.2015.7190490","DOIUrl":null,"url":null,"abstract":"This study investigates the effectiveness of the genetic algorithm evolved neural network and its application in the drive control systems of electromechanical objects. The methodology adopts a real coded GA strategy using datasets in a series of experiments that evaluate the effects on network performance of different choices of network parameters.","PeriodicalId":106868,"journal":{"name":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 XVIII International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCM.2015.7190490","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This study investigates the effectiveness of the genetic algorithm evolved neural network and its application in the drive control systems of electromechanical objects. The methodology adopts a real coded GA strategy using datasets in a series of experiments that evaluate the effects on network performance of different choices of network parameters.
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
