{"title":"Neural Networks in Designing Control Systems for Agro-Industrial Enterprises","authors":"Aleksandr Grachev","doi":"10.21603/2073-4018-2023-4-18","DOIUrl":null,"url":null,"abstract":"The paper introduces a comprehensive review of various approaches to using neural networks in the design of control systems for closed-end agricultural facilities. The empirical part of the study featured technical statistics of agro-industrial enterprises. It applied trained neural networks to agricultural enterprise data for prediction purposes. The resulting root mean square error was 0.120, and the standard deviation did not exceed 0.093. Neural networks proved efficient as part of specialized software for monitoring technical objects of the agro-industrial complex and predicting their development.","PeriodicalId":505709,"journal":{"name":"Cheese- and buttermaking","volume":"110 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cheese- and buttermaking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21603/2073-4018-2023-4-18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper introduces a comprehensive review of various approaches to using neural networks in the design of control systems for closed-end agricultural facilities. The empirical part of the study featured technical statistics of agro-industrial enterprises. It applied trained neural networks to agricultural enterprise data for prediction purposes. The resulting root mean square error was 0.120, and the standard deviation did not exceed 0.093. Neural networks proved efficient as part of specialized software for monitoring technical objects of the agro-industrial complex and predicting their development.
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
