首页 | 本学科首页   官方微博 | 高级检索  
     

基于径向基函数神经网络的板形模式识别研究
引用本文:张秀玲,陈丽杰,季颖,逄宗鹏. 基于径向基函数神经网络的板形模式识别研究[J]. 工业仪表与自动化装置, 2009, 0(3): 7-9
作者姓名:张秀玲  陈丽杰  季颖  逄宗鹏
作者单位:燕山大学,电气工程学院,工业计算机控制工程河北省重点实验室,河北,秦皇岛,066004
摘    要:针对板带轧制过程中用于辨识板形模式的网络精度较低、在线速度较慢和获得网络辨识模型较复杂的问题,提出了一种基于径向基函数神经网络(RBF)的板形模式识别方法。该方法使输入节点减少,网络模型简化,并用模糊C均值算法和伪逆法确定RBF网络的参数,解决了传统方法学习时间较长的问题。实验表明,该方法能有效的提高板形模式识别的精度和速度。

关 键 词:板形模式识别  RBF网络  模糊C均值算法  伪逆法

Research on the flatness pattern recognition based on radial basic function network
ZHANG Xiuling,CHEN Lijie,JI Ying,PANG Zongpeng. Research on the flatness pattern recognition based on radial basic function network[J]. Industrial Instrumentation & Automation, 2009, 0(3): 7-9
Authors:ZHANG Xiuling  CHEN Lijie  JI Ying  PANG Zongpeng
Affiliation:ZHANG Xiuling,CHEN Lijie,JI Ying,PANG Zongpeng(a.Department of Electronic Engineering,b.Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Hebei Qinhuangdao 066004,China)
Abstract:Flatness pattern recognition based on radial basic function(RBF) network was put forward in view of the feature that the accuracy of recognition network is low,the on-line speed is slow and the obtainment of network identification model is complex.This method basis fuzzy distance reduces the input node and makes the network model simple.And the method uses the fuzzy C-means clustering and the pseudo-inverse to determine the parameter of RBF network,which had solved the problem of the study time in tradition...
Keywords:flatness pattern recognition  RBF network  fuzzy c-means clustering  pseudo-inverse  
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号