An Improved Direct Neural Network Approach To Flatness Pattern Recognition Baseed On GA-RBF

被引：1
|
作者：
Feng Xiao-hua [1 ]
Sun Xiao-juan [1 ]
机构：
[1] Xian Technol Univ, Coll Elect Informat Engn, Xian 710032, Peoples R China
来源：
MANUFACTURING SCIENCE AND TECHNOLOGY, PTS 1-8 | 2012年 / 383-390卷
关键词：
Shape flateness; Pattern recognition; RBF network; Genetic algorithm;
D O I：
10.4028/www.scientific.net/AMR.383-390.2958
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, an improved the radial basis function (RBF) neural network direct recognition approach to shape flatness pattern is proposed. The genetic algorithm (GA) is employed to obtain more optimal structure and initial parameters of RBF network. The new approach with the advantages of RBF, such as fast learning and high accuracy, is efficient and intelligent, it can not only effectively settle the problem of the different topologic configurations with changing strip widths but also improve practicability and precision. Compared to the improved direct recognition method with GA-BP, The simulation results show that the speed and accuracy of the flatness pattern recognition model based on GA-RBF are obviously improved.
引用
收藏
页码：2958 / 2962
页数：5
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