{"title":"神经网络在农工企业控制系统设计中的应用","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":"{\"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}","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}
Neural Networks in Designing Control Systems for Agro-Industrial Enterprises
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.
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