摘要
本文提出基于核思想和贪婪算法的主元模糊神经网络模型,用来进一步提高转炉终点碳含量和温度预报模型的精度.采用核函数把输入变量向高维特征空间映射以充分挖掘变量的隐藏信息,经贪婪算法优化选取主元,除去变量的冗余信息,降低输入维数.将提取的主元输入自适应神经模糊推理系统后,网络以规则的形式来反映数据间蕴含的关系;以此模拟操作工经验,减少经验差异带来的影响.对转炉生产实测数据进行了仿真,结果表明该模型是有效的.
A principal component fuzzy neural network model based on kernel method and greedy algorithm is proposed, in order to improve the carbon contents of furnace endpoint and precision of the temperature focasting model. The model adopts kernel function to project the input variables into high dimensional feature space, so that the latent information can be extracted. Then greedy algorithm is used to select principal components, remove redundant information and reduce the input dimension. After the extracted principal components are introduced into the adaptive neuro-fuzzy inference system （ANFIS）, the network reveals the implication relations among the inputs by means of rules, so as to simulate experience of the operators and consequently to reduce the influence resulted from different operators. Simulations are made with practical data, and the result proves the validity of the proposed model.
出处
《信息与控制》
CSCD
北大核心
2008年第4期494-499,共6页
Information and Control
基金
国家863计划资助项目(2007AA04Z158)
国家自然科学基金资助项目(60674073)
国家科技支撑计划资助项目(2006BAB14B05)
国家973计划资助项目(2006CB403405)
关键词
转炉
核主元分析
贪婪算法
自适应神经模糊推理系统
终点预报
basic oxygen furnace
kernel principal component analysis, greedy algorithm
adaptive neuro-fuzzy inference system （ANFIS）
endpoint prediction
