シャ　キンカ   Jinhua She   佘　錦華    所属   工学部 機械工学科    職種   教授
言語種別	英語
発行・発表の年月	2016/12
形態種別	学術論文
査読	査読あり
標題	Discrete Wavelet Transfer Based BPNN for Calculating Carbon Efficiency of Sintering Process
執筆形態	共著
掲載誌名	Journal of Advanced Computational Intelligence and Intelligent Informatics
掲載区分	国内
出版社・発行元	Fuji Technology Press Ltd.
巻・号・頁	20(7),pp.1070-1076
担当範囲	All
著者・共著者	Xiaoxia Chen, Jinhua She, Xin Chen, and Min Wu
概要	Iron ore sintering process is the secondary most energy consuming procedure in steel making industry. In this study, a discrete wavelet transfer based back-propagation neural network (BPNN) model is built to predict the carbon efficiency of an iron ore sintering process. The raw-material variables and manipulated variables are chosen to be the inputs of the predictive model. First, the input variables are decomposed into 5 components. Then, BPNN models of each component are built. Finally, the prediction results are obtained by adding the output from each wave series. Actual run data are collected to verify the validity of the predictive model. The results show the validity of the proposed method with a MSE of 0.7708, a MAPE of 0.0125, and a R2 of 0.7016.
外部リンクURL	https://www.fujipress.jp/jaciii/jc/jacii002000071070/