Title :
Identification of the Hydraulic AGC System of Cold Rolling Mill using RBF Neural Network
Author :
Chen, Huiyong ; He, Shanghong
Author_Institution :
Inst. of Automobile & Mech. Eng., Changsha Univ. of Sci. & Technol.
Abstract :
The characteristics of hydraulic automatic gage control system (AGC) of cold rolling mill are analyzed. The principle of identification using radial basis function (RBF) network is introduced. The RBF network structure training algorithm is given in detail. Finally, the nonlinear model of HAGC is established with RBF network. To improve the accuracy of identification, the input and output data is filtered with wavelet denoising technique. The experimental results indicate that the combination of wavelet denoising and RBF network can provide an efficient method for system identification
Keywords :
cold rolling; hydraulic systems; identification; radial basis function networks; wavelet transforms; RBF network structure; RBF neural network; RBF training algorithm; cold rolling mill; hydraulic automatic gage control system; nonlinear model; radial basis function; system identification; wavelet denoising; Automobiles; Control systems; Electronic mail; Helium; Mechanical engineering; Milling machines; Neural networks; Noise reduction; Radial basis function networks; System identification; AGC system; RBF network; system identification; wavelet denoising;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712574
