{"title":"基于神经网络的废砖泡沫复合材料体积重量研究","authors":"A. Aliev, Yunis Gahramanli, S. Aliyev","doi":"10.32010/26166127.2022.5.1.87.93","DOIUrl":null,"url":null,"abstract":"This paper described the opportunity to use artificial neural networks to predict the chemical reaction result under given conditions. Applied three layers neural network for prediction of the mass content of alkaline trained using the results of the chemical reactions. As inputs were used values of the chemical quantities before the reaction and output values of the chemical quantities after the reaction. HPC technologies and multi-worker technology were used for accurate results.","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RESEARCH ON THE VOLUME WEIGHT OF FOAMED COMPOSITES BASED ON BRICK WASTE USING NEURAL NETWORKS\",\"authors\":\"A. Aliev, Yunis Gahramanli, S. Aliyev\",\"doi\":\"10.32010/26166127.2022.5.1.87.93\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper described the opportunity to use artificial neural networks to predict the chemical reaction result under given conditions. Applied three layers neural network for prediction of the mass content of alkaline trained using the results of the chemical reactions. As inputs were used values of the chemical quantities before the reaction and output values of the chemical quantities after the reaction. HPC technologies and multi-worker technology were used for accurate results.\",\"PeriodicalId\":275688,\"journal\":{\"name\":\"Azerbaijan Journal of High Performance Computing\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Azerbaijan Journal of High Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32010/26166127.2022.5.1.87.93\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Azerbaijan Journal of High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32010/26166127.2022.5.1.87.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RESEARCH ON THE VOLUME WEIGHT OF FOAMED COMPOSITES BASED ON BRICK WASTE USING NEURAL NETWORKS
This paper described the opportunity to use artificial neural networks to predict the chemical reaction result under given conditions. Applied three layers neural network for prediction of the mass content of alkaline trained using the results of the chemical reactions. As inputs were used values of the chemical quantities before the reaction and output values of the chemical quantities after the reaction. HPC technologies and multi-worker technology were used for accurate results.