卢虎生, 高斌, 赵利国, 国宏伟, 杨天钧. 高炉炉况判断神经网络专家系统[J]. 工程科学学报, 2002, 24(3): 276-279. DOI: 10.13374/j.issn1001-053x.2002.03.054
引用本文: 卢虎生, 高斌, 赵利国, 国宏伟, 杨天钧. 高炉炉况判断神经网络专家系统[J]. 工程科学学报, 2002, 24(3): 276-279. DOI: 10.13374/j.issn1001-053x.2002.03.054
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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
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高炉炉况判断神经网络专家系统

Neural Network Expert System of Forecasting Blast Furnace 0perational Conditions

    摘要
  • 摘要: 在深入分析高炉冶炼特点的基础上,提出泛化特性和自适应特性是高炉炉况判断系统稳定有效运行的2个重要特性.设计了增进系统泛化特性和自适应特性的方案,并相应开发出一套炉况判断专家系统.开发的系统在高炉上运行获得了满意效果.

     

    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.

     

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