DocumentCode :
3731231
Title :
The mill load modeling of combined grinding system based on RBF neural networks
Author :
Chuanjiang Yu; Jianjun Zheng; Tao Shen
Author_Institution :
School of Electrical Engineering, University of Jinan, China
fYear :
2015
Firstpage :
2085
Lastpage :
2090
Abstract :
In order to get the mill load modeling of combined grinding system in normal working condition, this paper proposes a method based on the RBF neural network. The neural network uses three kinds of kernel functions that are Gauss kernel function, multiquadric kernel function and inverse multinuclear kernel function. Using the gradient descent method trains the neural network. With the comparison of three neural network´s fitting error, I´ve come to the conclusion that the RBF neural network based on Gauss function is more accurate.
Keywords :
"Artificial neural networks","Training"
Publisher :
ieee
Conference_Titel :
Chinese Automation Congress (CAC), 2015
Type :
conf
DOI :
10.1109/CAC.2015.7382848
Filename :
7382848
Link To Document :
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