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模糊C均值聚類算法在高爐料面分類中的應用

Application of the fuzzy C-means clustering algorithm in blast furnace burden surface identification

  • 摘要: 通過對多雷達掃描得到的高爐料面進行數據處理,根據數據的特征,分別采用模糊C均值聚類和特征加權模糊C均值聚類算法對料面數據進行分類,建立標準料面模型庫.再通過模糊模式識別中貼近度的方法把待分類的目標料面與模型庫相匹配,為后續的布料控制提供依據.該算法在某2500 m3高爐上進行了實驗,取得良好的效果.仿真結果表明了其有效性.

     

    Abstract: Blast furnace burden surface data derived from multi radars were processed. Fuzzy C-means and feature weighted fuzzy C-means clustering were applied to identify the burden surface data according to the data information, and a standard burden surface model database was set up. Each target burden surface was matched with the model database by using the method of nearness in fuzzy pattern recognition, and this provides a basis for the next burden surface control. The algorithm was carried out into a 2 500 m3 blast furnace, and the control effect has been improved. The simulation results show the effectiveness of the proposed method.

     

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