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數據挖掘在安鋼電極預測建模中的應用

Application of data mining in electrode prediction modeling of Anyang Steel

  • 摘要: 從安鋼電極控制的實際應用出發,應用數據挖掘技術建立了電極預測模型并應用于電極控制系統的參數整定.首先介紹了建立電極預測模型的數據挖掘過程;然后在數據挖掘算法中提出了一種新的變結構遺傳Elman網絡方法,該算法用改進的混合遺傳算法對網絡結構和權值及自反饋增益同步動態尋優.將基于BP算法的Elman網絡和本文提出的變結構遺傳Elman網絡都應用于安鋼交流電弧爐的電極預測模型中進行比較.通過基于安鋼現場數據的計算機仿真實驗表明:采用變結構遺傳Elman網絡的數據挖掘算法比BP算法具有更好的動態性能、更快的逼近速度和更高的精度.在此基礎上,把建立的模型應用于安鋼電極控制系統的參數整定,取得了良好的控制效果.

     

    Abstract: On the basis of electrode control in Anyang Steel, a prediction model was established by adopting data mining technique and applied to parameter tuning of an electrode control system. First the data mining process of the electrode prediction model was introduced. A variable structure generic Elman neural network, which can evolve the network structure, the weights and self-feedback gain coefficient simultaneously, was proposed based on a new hybrid generic algorithm and data mining algorithm. The Elman based on BP algorithm and the variable structure generic Elman neural network were applied to establishing of an electrode prediction model for Anyang Steel. The simulation results based on the spot real data of Anyang Steel show that data mining algorithm combined with the variable structure generic Elman neural network has better dynamic characteristic, faster approach speed, better precision than BP algorithm. Finally, when this model was applied to parameter tuning of the electrode control system in Anyang Steel, its control effeet was remarkable.

     

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