A. Dariouchy, E. Aassif, G. Maze, R. Latif, D. Decultot, M. Laaboubi
{"title":"用神经网络方法预测钢管背散射声压","authors":"A. Dariouchy, E. Aassif, G. Maze, R. Latif, D. Decultot, M. Laaboubi","doi":"10.1109/ISCIII.2007.367373","DOIUrl":null,"url":null,"abstract":"A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.","PeriodicalId":314768,"journal":{"name":"2007 International Symposium on Computational Intelligence and Intelligent Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Prediction of the Acoustic Pressure Backscattered by a Steel Tube Using Neural Networks Approach\",\"authors\":\"A. Dariouchy, E. Aassif, G. Maze, R. Latif, D. Decultot, M. Laaboubi\",\"doi\":\"10.1109/ISCIII.2007.367373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.\",\"PeriodicalId\":314768,\"journal\":{\"name\":\"2007 International Symposium on Computational Intelligence and Intelligent Informatics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Symposium on Computational Intelligence and Intelligent Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCIII.2007.367373\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Symposium on Computational Intelligence and Intelligent Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCIII.2007.367373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of the Acoustic Pressure Backscattered by a Steel Tube Using Neural Networks Approach
A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.