东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (8): 1083-1086.DOI: -

• 论著 • 上一篇    下一篇

基于微分进化的钢水温度预报混合模型

袁平;毛志忠;王福利;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-08-15 发布日期:2013-06-22
  • 通讯作者: Yuan, P.
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2007AA04Z194)

Hybrid modeling based on differential evolutionary algorithm for prediction of molten steel temperature

Yuan, Ping (1); Mao, Zhi-Zhong (1); Wang, Fu-Li (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-08-15 Published:2013-06-22
  • Contact: Yuan, P.
  • About author:-
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摘要： 针对LF冶炼过程钢水温度预报问题对机理模型进行了分析.将基于微分进化的BP网络引入混合模型,以确定模型中难以准确获得的参数,建立了基于微分进化的钢水温度混合预报模型.模型通过适当时刻变异因子的随机选取和重复进行种群个体初始化的方法,对微分进化算法进行了改进,有效解决了BP网络训练的困难,避免了算法早熟.实验结果表明,混合模型具有较好的预测结果,基本满足了实际生产的需要.

关键词: 钢包炉(LF), 钢水温度预报, 混合建模, 微分进化算法, BP神经网络

Abstract: To predict the molten steel temperature in the smelting process of ladle furnace (LF), a hybrid model is proposed, where a BP neural network trained by an improved differential evolutionary algorithm (DEA) is introduced to determine the model parameters which are hard to exactly obtain by mechanism models. In the model, the mutation factor is randomly selected and the individuals in population are re-initialized in due time, thus rising above the difficulty efficiently in training BP neural network and avoiding the premature of DEA. Simulation results showed that the hybrid model has good precision of prediction for molten steel temperature and meets practical production requirements.

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