铁水硅含量的集成模糊神经网络预测方法  

Research on hot metal silicon content prediction based on integrated neural network

作　　者：李界家[1] 杨志宇[1] 曹阳[1] 

机构地区：[1]沈阳建筑大学信息与控制工程学院,辽宁沈阳110168

出　　处：《计算机与应用化学》2013年第10期1113-1116,共4页Computers and Applied Chemistry

基　　金：国家自然科学基金资助项目(60874103)

摘　　要：在炼铁生产过程中,有效的降低铁水硅含量并且使其保持在合理的低水平,有利于提高生铁的质量和产量。但是在实际生产过程中,生铁的硅含量只有在生铁样本送到实验室经过化验后方可得知,检测存在严重的滞后性,这样采取的调整铁水硅含量的措施也会滞后。为了消除检测的滞后性,及时的对铁水硅含量的调整采取及时有效的措施,有必要对铁水硅含量进行预测。基于模块化和信息融合思想,本文提出了集成模糊神经网络铁水硅含量预测方法,选取了2个预测子模块单独学习并训练,然后经过一个决策融合模块得到最终的铁水硅含量预测结果。建立了铁水硅含量预测模型,并在模型训练完成后进行了MATLAB实验仿真。仿真数据采集自凌源钢厂2号高炉,样本数据均在高炉炉况基本稳定的运行条件下获得,用这些样本数据训练预测模型。预测模型训练结束后,又选取50个样本对模型进行测试预测。仿真结果验证了该方法的有效性,集成模糊神经网络预测模型预测精度很高,相对误差较小,能够给予高炉生产过程给予有效的指导。Reducing silicon content in hot metal effectively and keeping it in a reasonable low level have significant impact. However, the silicon content in hot metal is not known until the steel samples are sent to laboratory to be tested. So, it is necessary to predict silicon content advanced. A method of modular integrated fuzzy neural network prediction of silicon content in hot metal is proposed based on the theory of modularization. To improve forecasting accuracy and decrease training time, integration method is also leaded into this paper. The whole forecasting network is divided into two sub-networks. These two sub-networks are trained respectively. Through this way, the training time is decreased by a large margin. Forecasting accuracy and system stability are all improved effectively at the same time. Date samples are acquired from a domestic steel blast furnace which runs in a stable operating condition. These samples are used to train the model. After training, 50 samples are selected to test the prediction model. The prediction results show that the method has good prediction effect.

关 键 词：硅含量预测 模糊神经网络 RBF神经网络 集成预测模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]