Application of data mining in electrode prediction modeling of Anyang Steel
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摘要: 从安钢电极控制的实际应用出发,应用数据挖掘技术建立了电极预测模型并应用于电极控制系统的参数整定.首先介绍了建立电极预测模型的数据挖掘过程;然后在数据挖掘算法中提出了一种新的变结构遗传Elman网络方法,该算法用改进的混合遗传算法对网络结构和权值及自反馈增益同步动态寻优.将基于BP算法的Elman网络和本文提出的变结构遗传Elman网络都应用于安钢交流电弧炉的电极预测模型中进行比较.通过基于安钢现场数据的计算机仿真实验表明:采用变结构遗传Elman网络的数据挖掘算法比BP算法具有更好的动态性能、更快的逼近速度和更高的精度.在此基础上,把建立的模型应用于安钢电极控制系统的参数整定,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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Keywords:
- data mining /
- electrode /
- variable structure /
- hybrid generic algorithm /
- Elman neural network
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