基于PCA-GA-BP的TPC受铁量预测模型

HUANG Ying-lei,LIU Ye-feng,HUANG Hui,ZHENG Bing-lin

Control Engineering of China（2009）

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Abstract

预测鱼雷罐车(TPC)在高炉的实际受铁量,对协调铁水平衡、减少兑罐次数和温降损失,保证高炉出铁安全,提高TPC利用率具有重要作用。采用主成分分析(PCA)提取过程特征参数,并剔除相关冗余信息;BP神经网络用来逼近受铁量预测这一非线性过程;改进了遗传算法(GA)的适应度函数,并精确给定BP神经网络的权值和阈值,进而建立了基于PCA-GA-BP的TPC受铁量预测模型。采用某钢铁企业实际生产数据运算,结果表明模型合理、有效,提高了鱼雷罐车(TPC)受铁量预测准确性。

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Key words

reception iron amount,genetic algorithm,principle component analysis,torpedo ladle car,back-propagation neural network

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