{"title":"Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material","authors":"Qing-liang CHANG, Hua-qiang ZHOU, Chao-jiong HOU","doi":"10.1016/S1006-1266(08)60292-8","DOIUrl":null,"url":null,"abstract":"<div><p>In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural networks. Based on cases related to our test data of filling material, the predicted results of the model and measured values are compared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.</p></div>","PeriodicalId":15315,"journal":{"name":"Journal of China University of Mining and Technology","volume":"18 4","pages":"Pages 551-555"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1006-1266(08)60292-8","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of China University of Mining and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1006126608602928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural networks. Based on cases related to our test data of filling material, the predicted results of the model and measured values are compared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.