寶鋼IF鋼大生產產品性能預測

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寶鋼IF鋼大生產產品性能預測

初元璋, 祁鵬, 張婭

文章導航 > 工程科學學報 > 2001 > 23(1): 48-51

初元璋, 祁鵬, 張婭. 寶鋼IF鋼大生產產品性能預測[J]. 工程科學學報, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

引用本文:

初元璋, 祁鵬, 張婭. 寶鋼IF鋼大生產產品性能預測[J]. 工程科學學報, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

CHU Yuanzhang, QI Peng, ZHANG Ya. Performance Forecast of IF Steel Mass-Produced in Bao Steel[J]. Chinese Journal of Engineering, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

Citation:

CHU Yuanzhang, QI Peng, ZHANG Ya. Performance Forecast of IF Steel Mass-Produced in Bao Steel[J]. Chinese Journal of Engineering, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

初元璋, 祁鵬, 張婭. 寶鋼IF鋼大生產產品性能預測[J]. 工程科學學報, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

引用本文:

初元璋, 祁鵬, 張婭. 寶鋼IF鋼大生產產品性能預測[J]. 工程科學學報, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

CHU Yuanzhang, QI Peng, ZHANG Ya. Performance Forecast of IF Steel Mass-Produced in Bao Steel[J]. Chinese Journal of Engineering, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

Citation:

CHU Yuanzhang, QI Peng, ZHANG Ya. Performance Forecast of IF Steel Mass-Produced in Bao Steel[J]. Chinese Journal of Engineering, 2001, 23(1): 48-51. doi: 10.13374/j.issn1001-053x.2001.01.040

寶鋼IF鋼大生產產品性能預測

doi: 10.13374/j.issn1001-053x.2001.01.040

北京科技大學材料科學與工程學院, 北京 100083

基金項目:

國家"九五"攻關資助項目（No.9527-01-01-09）

作者簡介:
初元璋男,54歲,副教授

中圖分類號: TG334.9
計量
- 文章訪問數: 114
- HTML全文瀏覽量: 11
- PDF下載量: 9
- 被引次數: 0
出版歷程
- 收稿日期: 2000-07-04

Performance Forecast of IF Steel Mass-Produced in Bao Steel

Material Science and Engineering School, UST Beijing, Beijing 100083, China

摘要: 以BP算法為基礎開發了ANN學習預測系統,用于寶鋼IF鋼大生產產品性能預測.同時,應用在寶鋼IF鋼大生產數據對該系統進行了測試和分析,并與多元線性回歸結果進行預測精度比較.結果表明,ANN學習預測系統,除σ_0.2誤差較高(9.0%)外,σ_b,δ,r和n值均<5.0%,且比多線性回歸方法精度高.
- BP算法 /
- IF鋼 /
- 性能預測
Abstract: Develop ANN learn-forecast system by employing BP algorithm to forecast the performance of IF steel, test and analyze the system by using data collected from BAO Steel, and compare the precision of forecasted data with that of the multivariant linear regression model. The results show that the relative errors of ANN learn-forecast system on σ_b,δ₁, r and n are all less than 5.0% except that on σ_0.2 is 9.0%. It is concluded that this system has a higher forecast precision than the multivariant linear regression model.
- BP algorithm /
- IF steel /
- performance forecast

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文章訪問數: 114
HTML全文瀏覽量: 11
PDF下載量: 9
被引次數: 0

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