DocumentCode
2748308
Title
Wavelet Neural Network-based Control Chart Patterns Recognition
Author
Wu, Shaoxiong ; Wu, Biying
Author_Institution
Dept. of Econ. & Manage., Fujian Univ. of Technol., Fuzhou
Volume
2
fYear
0
fDate
0-0 0
Firstpage
9718
Lastpage
9721
Abstract
As for six patterns of control chart, wavelet neural network was presented. The structure of wavelet neural network was made up of three-layer network, of which there were 24 neurons of input layer, 20 neurons of hidden layer as well as 6 neurons of output layer. Mexican Hat wavelet was selected as activation function. The simulation results show that wavelet neural network have many advantages, such as quicker training and better recognition performance and better convergence than BP networks, which can be used in control chart recognition
Keywords
control charts; neural nets; pattern recognition; wavelet transforms; Mexican hat wavelet; activation function; control chart pattern recognition; wavelet neural network; Artificial neural networks; Automatic control; Computer integrated manufacturing; Control charts; Convergence; Expert systems; Neural networks; Neurons; Pattern recognition; Quality control; Control chart; pattern recognition; wavelet neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1713890
Filename
1713890
Link To Document