人工神经网络技术在冶金过程终点预报应用方面具有广阔的应用前景。
There is the better application prospect for the application of artificial neural network in end-point prediction for the whole ferrous metallurgy process.
本文以转炉吹炼工业生产过程为背景，着重研究转炉吹炼终点预报模型的建立和应用。
On the background of production process of matte converting, the endpoint predictive model is established and applied.
然后通过对真空感应冶炼工艺机理的深入分析以及对神经网络算法的研究，建立了基于RBF神经网络的终点预报模型。
Then an end-point forecast model was built based on the RBF neural network by analyzing the melting technology and the RBF training algorithm.
研究表明，本模型能够对终点磷含量进行很好的预报和控制。
The results showed that this model is good for prediction and control of end phosphorus content in BOF process.
结果表明，此法能有效提高对转炉终点碳预报的命中率和网络的训练速度。
The results show that this method can effectively enhance the hit rate of end-point carbon prediction and the training speed of network.
由于BP神经网络可以实现任意线性或非线性的函数映射，所以可以满足终点氧含量的预报要求。
Because the BP neural network may realize the free linearity or the non-linear function mapping, it may satisfy the request of the forecast for the endpoint oxygen content.
根据生产统计数据，建立了转炉终点氧预报模型。
The BOF end-point oxygen content predicting model is established according to statistics of production data.
基于理论分析和现场经验数据，建立了ANS—OB精炼终点温度预报模型。
The forecast model of the end point temperature of ANS - OB refining is established based on the theoretical analysis and on - site empirical data.
将该方法应用于转炉终点磷含量预报模型，取得了较好的结果。
The proposed approach is used successfully for the prediction of end phosphorus content in convert.
针对真空感应炉生产过程中温度测量成本较高及精度较差等不足，建立了基于RBF神经网络的真空感应炉终点钢水温度预报模型。
A prediction model of molten steel temperature based on RBF neural network was developed to reduce cost and improve temperature control accuracy for vacuum induction melting.
提出了基于改进的BP神经网络学习算法和自适应残差补偿算法的炼铜转炉吹炼终点组合预报模型。
It is the first time that a converting furnace endpoint prediction model based on an improved BP neural network and error compensation of linear regression.
准确预报转炉冶炼终点的钢水温度与碳含量对提高转炉终点命中率具有重要意义。
Accurate prediction of the end-point temperature and carbon content of BOF molten steel is of great significance to raising the hitting rate of the end-point.
采用数据分析的方法建立了废气温度上升点、烧结终点、垂直烧结速度等参数的预报模型；
Predicted models of rising position of gas temperature, burn through point, vertical sintering speed, and so on, are established using dada analysis method.
采用BP神经网络，建立炼铜转炉吹炼造铜期终点与各影响因素之间的数学模型，对吹炼终点进行预报。
A mathematical model of endpoint and correlative factors is developed to predict the endpoint of matte-converting using the BP neural network model.
采用BP神经网络，建立炼铜转炉吹炼造铜期终点与各影响因素之间的数学模型，对吹炼终点进行预报。
A mathematical model of endpoint and correlative factors is developed to predict the endpoint of matte-converting using the BP neural network model.
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