| 基于正则化RBF神经网络的钢包精炼炉电极系统智能建模 |
| |
| 引用本文： | 储岳中,张绍德,张世峰.基于正则化RBF神经网络的钢包精炼炉电极系统智能建模[J].自动化与仪表,2004,19(5):5-7,11. |
| |
| 作者姓名： | 储岳中 张绍德 张世峰 |
| |
| 作者单位： | 安徽工业大学,电气信息学院,安徽,马鞍山,243002 |
| |
| 基金项目： | 安 徽省 “十五”攻关项 目(01012053) |
| |
| 摘 要： | 通过RBF神经网络和模糊推理系统的比较，得出正则化RBF神经网络的输出特性，在此基础上利用改进的最近邻聚类算法确定网络的隐层节点个数和高斯函数中心，并估计输出层权值。仿真结果表明了所提方案的有效性。
|
| 关 键 词： | 正则化 RBF神经网络 钢包精炼炉 模糊推理 最近邻聚类算法 |
| 文章编号： | 1001-9944(2004)05-0005-04 |
Intelligent Modeling for the Electrode System in Ladle Furnace Based on Regular RBF Neural Network |
| |
| CHU Yue-zhong,ZHANG Shao-de,Zhang Shi-feng.Intelligent Modeling for the Electrode System in Ladle Furnace Based on Regular RBF Neural Network[J].Automation and Instrumentation,2004,19(5):5-7,11. |
| |
| Authors: | CHU Yue-zhong ZHANG Shao-de Zhang Shi-feng |
| |
| Abstract: | The output character of regular Radial Basis Function Neural Network (RBFNN) are gained by the comparison of RBFNN and fuzzy inference system(FIS).The hidden layer node number and the center of Gauss function of RBFNN are verified by improved nearest neighber-clustering algorithm,and predicated the power of output layer.The simulation result shows the effectiveness of the proposed scheme. |
| |
| Keywords: | regular RBFNN ladle furnace fuzzy inference nearest neighber-clustering algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
