{"title":"飞机杠杆浮阀的神经网络计算","authors":"D. Pushkarev, D. Y. Kiselev, Y. Kiselev","doi":"10.18287/2541-7533-2022-21-4-44-51","DOIUrl":null,"url":null,"abstract":"The possibility of using neural networks in aviation is shown, in particular in products intended for use in aviation technology. The possibility of using neural networks throughout the entire life cycle of aviation equipment products is analyzed. The advantages that can be obtained using neural networks are described. The main stages of creating a neural network model are analyzed and a description of each stage is presented. The difficulties associated with the practical application of models based on artificial intelligence are shown. The calculation of the operation of a lever-float valve is presented and a neural network model is made for its calculation using real operation and test data.","PeriodicalId":265584,"journal":{"name":"VESTNIK of Samara University. Aerospace and Mechanical Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Calculation of aircraft lever-float valves using neural networks\",\"authors\":\"D. Pushkarev, D. Y. Kiselev, Y. Kiselev\",\"doi\":\"10.18287/2541-7533-2022-21-4-44-51\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The possibility of using neural networks in aviation is shown, in particular in products intended for use in aviation technology. The possibility of using neural networks throughout the entire life cycle of aviation equipment products is analyzed. The advantages that can be obtained using neural networks are described. The main stages of creating a neural network model are analyzed and a description of each stage is presented. The difficulties associated with the practical application of models based on artificial intelligence are shown. The calculation of the operation of a lever-float valve is presented and a neural network model is made for its calculation using real operation and test data.\",\"PeriodicalId\":265584,\"journal\":{\"name\":\"VESTNIK of Samara University. Aerospace and Mechanical Engineering\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VESTNIK of Samara University. Aerospace and Mechanical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18287/2541-7533-2022-21-4-44-51\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VESTNIK of Samara University. Aerospace and Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18287/2541-7533-2022-21-4-44-51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Calculation of aircraft lever-float valves using neural networks
The possibility of using neural networks in aviation is shown, in particular in products intended for use in aviation technology. The possibility of using neural networks throughout the entire life cycle of aviation equipment products is analyzed. The advantages that can be obtained using neural networks are described. The main stages of creating a neural network model are analyzed and a description of each stage is presented. The difficulties associated with the practical application of models based on artificial intelligence are shown. The calculation of the operation of a lever-float valve is presented and a neural network model is made for its calculation using real operation and test data.