信息与控制
信息與控製
신식여공제
INFORMATION AND CONTROL
2010年
1期
82-87
,共6页
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
轉爐%終點預報%獨立成分分析%微粒群優化算法%徑嚮基函數神經網絡
전로%종점예보%독립성분분석%미립군우화산법%경향기함수신경망락
basic oxygen furnace%endpoint prediction%independent component analysis%particle swarm optimization%radial basis function neural network
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小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.
提齣將微粒群優化算法和獨立成分分析引入到徑嚮基函數神經網絡模型用于轉爐煉鋼終點預報.利用微粒群優化算法的全跼遍歷特性和快速小動點算法的跼部尋優能力,改進瞭傳統的獨立成分分析算法,解決瞭其目標函數易陷入跼部最優和獨立特徵排序不確定的問題,壓縮冗餘信息併降低輸入維數.將提取齣的獨立特徵輸入徑嚮基函數神經網絡,預報終點溫度和碳含量.對轉爐生產實測數據進行瞭倣真,結果錶明該模型能有效提高預報精度,保證預報的可靠性.
제출장미립군우화산법화독립성분분석인입도경향기함수신경망락모형용우전로련강종점예보.이용미립군우화산법적전국편력특성화쾌속소동점산법적국부심우능력,개진료전통적독립성분분석산법,해결료기목표함수역함입국부최우화독립특정배서불학정적문제,압축용여신식병강저수입유수.장제취출적독립특정수입경향기함수신경망락,예보종점온도화탄함량.대전로생산실측수거진행료방진,결과표명해모형능유효제고예보정도,보증예보적가고성.
A radial basis function neural network model combined with particle swarm optimization algorithm and independent 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 information 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.
