{"title":"人工神经网络分析在热系统中的应用","authors":"R. Mahajan, K. T. Yang","doi":"10.1115/imece2000-1469","DOIUrl":null,"url":null,"abstract":"\n Artificial neural networks (ANNs) are good at approximating complex and non-linear data. In addition, they have excellent predictive capabilities and can be configured to be self-adaptive. As a result of these characteristics, the potential applications of ANNs are many and in diverse fields. These range from predicting the output of a manufacturing process through differentiating between handwritten letters to predicting the winner of a horse race. In this paper, we focus on applications of artificial neural networks to thermal systems including chemical vapor deposition, thermal management and heat exchangers.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applications of Artificial Neural Network Analysis in Thermal Systems\",\"authors\":\"R. Mahajan, K. T. Yang\",\"doi\":\"10.1115/imece2000-1469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Artificial neural networks (ANNs) are good at approximating complex and non-linear data. In addition, they have excellent predictive capabilities and can be configured to be self-adaptive. As a result of these characteristics, the potential applications of ANNs are many and in diverse fields. These range from predicting the output of a manufacturing process through differentiating between handwritten letters to predicting the winner of a horse race. In this paper, we focus on applications of artificial neural networks to thermal systems including chemical vapor deposition, thermal management and heat exchangers.\",\"PeriodicalId\":306962,\"journal\":{\"name\":\"Heat Transfer: Volume 3\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Heat Transfer: Volume 3\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/imece2000-1469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Heat Transfer: Volume 3","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/imece2000-1469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applications of Artificial Neural Network Analysis in Thermal Systems
Artificial neural networks (ANNs) are good at approximating complex and non-linear data. In addition, they have excellent predictive capabilities and can be configured to be self-adaptive. As a result of these characteristics, the potential applications of ANNs are many and in diverse fields. These range from predicting the output of a manufacturing process through differentiating between handwritten letters to predicting the winner of a horse race. In this paper, we focus on applications of artificial neural networks to thermal systems including chemical vapor deposition, thermal management and heat exchangers.