[1]ZHANG Xiu-ling,ZHANG Zhi-qiang.Simulation research on strip flatness and thickness control? based on dynamic RBF neural networks[J].CAAI Transactions on Intelligent Systems,2007,2(2):65-68.
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Simulation research on strip flatness and thickness control? based on dynamic RBF neural networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 2 Page number: 65-68 Column: 学术论文—机器学习 Public date: 2007-04-25
Title:
Simulation research on strip flatness and thickness control? based on dynamic RBF neural networks
Author(s):
ZHANG Xiu-ling; ZHANG Zhi-qiang
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Chin a
Keywords:
RBFNN;  dynamic design;  inverse matrix;  integrated control of strip fla tness and thickness
CLC:
TP183
DOI:
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Abstract:
A method to dynamically adjust the number of hidden layer nodes is pro posed based on features of the RBFNN, which includes two parts: the first part i s to adjust the number of hidden layer nodes based on the mean square error and change rate of network output data, and the second part is to optimize the centr al v alue of the hidden layer and find the output layer’s weights based on the gener alized inverse matrix. The newly designed RBFNN has least nodes of hidden layers and higher training speed. A mathematical model for controlling strip flatness and thickness is proposed. Control simulation is executed with dynamic RBF neur al network based on new model, receiving an ideal result.
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