摘要
提出将微粒群优化算法和独立成分分析引入到径向基函数神经网络模型用于转炉炼钢终点预报.利用微粒群优化算法的全局遍历特性和快速不动点算法的局部寻优能力,改进了传统的独立成分分析算法,解决了其目标函数易陷入局部最优和独立特征排序不确定的问题,压缩冗余信息并降低输入维数.将提取出的独立特征输入径向基函数神经网络,预报终点温度和碳含量.对转炉生产实测数据进行了仿真,结果表明该模型能有效提高预报精度,保证预报的可靠性.
A radial basis function neural network model combined with particle swarm optimization algorithm and inde- pendent component analysis is proposed to predict the endpoint of BOF （basic oxygen furnace）steelmaking. In order to solve the issues that the objective function falls into the local optimum and the sequence of independent components is uncertain, this paper utilizes the global ergodicity of particle swarm optimization algorithm and the local optimizing capacity of fast fixed-point algorithm to improve the traditional independent component analysis algorithm, as well as the redundant infor- mation is compressed and the input dimension is reduced. The extracted independent features are introduced into the radial basis function neural network to predict the endpoint temperature and carbon content. Simulations are made with the practical data of BOF production, and the result proves the proposed model can improve the accuracy and reassure the reliability of prediction.
出处
《信息与控制》
CSCD
北大核心
2010年第1期82-87,共6页
Information and Control
基金
国家863计划资助项目(2007AA04Z158)
国家科技支撑计划资助项目(2006BAB14B05)
国家973计划资助项目(2006CB403405)
国家自然科学基金资助项目(60674073)
关键词
转炉
终点预报
独立成分分析
微粒群优化算法
径向基函数神经网络
basic oxygen furnace
endpoint prediction
independent component analysis
particle swarm optimization
radial basis function neural network
