%0 Journal Article
%T An improved neural network method for the prediction of comprehensive production indices in coking process<br>炼焦生产过程综合生产指标的改进神经网络预测方法
%A WANG Wei
%A WU Min
%A LEI Qi
%A CAO Wei-hua
%A <br>王伟
%A 吴敏
%A 雷琪
%A 曹卫华
%J 控制理论与应用
%D 2009
%I 
%X A prediction method based on the improved back propagation(BP) neural network is proposed to solve the problem of large time-delay in the detection of the comprehensive production indices (quality and quantity of coke, and energy consumption of coke oven) in the coking process. First, the input and output variables of the prediction models are determined by analyzing the process mechanism correlation between process parameters based on principal components analysis and grey relational analysis. Then, the BP neural network based on an improved differential evolution algorithm is applied to establish prediction models, which are compared with the basic BP neural network prediction models. Finally, the prediction models are verified. Simulation results show that the proposed prediction models provide a better convergence rate and higher prediction accuracy, and the prediction effect of the obtained models satisfy the technological requirements.
%K coking process
%K principal component analysis
%K grey relational analysis
%K improved differential evolution algorithm
%K improved BP neural network
%K prediction model<br>炼焦生产过程
%K 主元分析
%K 灰色关联分析
%K 改进差分进化算法
%K 改进BP神经网络
%K 预测模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=F55191ABC3D3C0D8F23F7EBF90F53C92&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=59906B3B2830C2C5&sid=8EA44A8F6C7F424F&eid=AFD02B86BFB3C7FC&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=15