Prediction on the starting temperature of molten steel in second refining by using case-based reasoning
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摘要: 針對BP神經網絡訓練時間長的問題,采用基于案例推理的方法預測精煉開始鋼水溫度.首先,應用層次分析法確定影響精煉開始鋼水溫度的各個因素的權值,并使用灰色關聯度來計算案例的相似度,克服了傳統相似度計算方法在案例信息不完整的情況下無法獲取準確結果的缺點.然后,提出一個包含類選、粗選、精選和擇優的四步檢索方法,大大縮短了檢索時間.最后,實驗比較了人工神經網絡和基于案例推理兩種方法,結果表明基于案例推理比人工神經網絡具有更高的命中率.Abstract: Case-based reasoning was used to predict the starting temperature of molten steel in second refining so as to avoid the long training time of a BP (back propagation) neural network. Analytic hierarchy process (AHP) was applied to determine the weights of factors influencing the starting temperature. Grey relational degree was adopted to compute the similarity between cases. Thus the shortcoming of difficulty in obtaining accurate cases with incomplete information is conquered. A four-step search method, including class search, rough search, delicate search, and optimized search, was provided, by which the search time decreases greatly. Experimental results using both artificial neural networks and case-based reasoning were compared. It is shown that case-based reasoning has got a higher hit rate and a shorter response time than artificial neural networks.
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Key words:
- steelmaking /
- refining /
- temperature /
- prediction /
- case-based reasoning
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