高爐爐況判斷神經網絡專家系統

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高爐爐況判斷神經網絡專家系統

盧虎生, 高斌, 趙利國, 國宏偉, 楊天鈞

文章導航 > 工程科學學報 > 2002 > 24(3): 276-279

盧虎生, 高斌, 趙利國, 國宏偉, 楊天鈞. 高爐爐況判斷神經網絡專家系統[J]. 工程科學學報, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

引用本文:

盧虎生, 高斌, 趙利國, 國宏偉, 楊天鈞. 高爐爐況判斷神經網絡專家系統[J]. 工程科學學報, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

LU Husheng, GAO Bin, ZHAO Liguo, GUO Hongwei, YANG Tianjun. Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions[J]. Chinese Journal of Engineering, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

Citation:

LU Husheng, GAO Bin, ZHAO Liguo, GUO Hongwei, YANG Tianjun. Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions[J]. Chinese Journal of Engineering, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

盧虎生, 高斌, 趙利國, 國宏偉, 楊天鈞. 高爐爐況判斷神經網絡專家系統[J]. 工程科學學報, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

引用本文:

盧虎生, 高斌, 趙利國, 國宏偉, 楊天鈞. 高爐爐況判斷神經網絡專家系統[J]. 工程科學學報, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

LU Husheng, GAO Bin, ZHAO Liguo, GUO Hongwei, YANG Tianjun. Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions[J]. Chinese Journal of Engineering, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

Citation:

LU Husheng, GAO Bin, ZHAO Liguo, GUO Hongwei, YANG Tianjun. Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions[J]. Chinese Journal of Engineering, 2002, 24(3): 276-279. doi: 10.13374/j.issn1001-053x.2002.03.054

高爐爐況判斷神經網絡專家系統

doi: 10.13374/j.issn1001-053x.2002.03.054

1. 北京科技大學信息工程學院, 北京 100083;
2. 包頭鋼鐵學院, 包頭 014010;
3. 北京科技大學冶金學院, 北京 100083

基金項目:

國家重點科技項目(No.97-562-02-02)

作者簡介:
盧虎生, 男,37歲,副教授

中圖分類號: TF543.1;TP39L
計量
- 文章訪問數: 177
- HTML全文瀏覽量: 29
- PDF下載量: 6
- 被引次數: 0
出版歷程
- 收稿日期: 2001-07-04
- 網絡出版日期: 2021-08-21

Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions

1. Information Engineering School, UST Beijing, Beijing 100083, China;
2. Baotou University of Iron and Steel, Baotou 014010, China;
3. Metallurgy School, UST Beijing, Beijing 100083, China

摘要: 在深入分析高爐冶煉特點的基礎上,提出泛化特性和自適應特性是高爐爐況判斷系統穩定有效運行的2個重要特性.設計了增進系統泛化特性和自適應特性的方案,并相應開發出一套爐況判斷專家系統.開發的系統在高爐上運行獲得了滿意效果.
- 專家系統 /
- 神經網絡 /
- 高爐 /
- 泛化性 /
- 自適應性
Abstract: Based on deeply-analyzing the characteristics of iron-making process, it is presented that gen-eralization and self-adaptation of the BF judgement systems are two important factors for maintaining the sta-bility and efficiency of neural network expert system. The strategy for improving these two features has been proposed and a new developed system has been proved to be satisfactory in the on-site blast furnace operation.
- expert system /
- neural network /
- blast furnace /
- generalization /
- selp-adaptation

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計量

文章訪問數: 177
HTML全文瀏覽量: 29
PDF下載量: 6
被引次數: 0

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