Study of BBD Ball Mill Material Measure Based on Rough Sets and RBF Neural Network Data Fusion

In this paper, the material measure of BBD ball mill based on multi-information data fusion is researched. By combining the Rough Set and Radial Basis Function neural network, this method can not only solve the priori difficulty in obtaining information in data fusion and a large number of redundant...

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Bibliographic Details
Main Authors: Bao-xia Cui, Xing-yu Qu, Yong Duan
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Area measurement
Ball milling
BBD ball mill
Convergence
Data engineering
Function approximation
Information systems
material measure
multi-information data fusion
Neural networks
RBF neuralnetwork
rough set
Rough sets
Systems engineering and theory
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Summary:In this paper, the material measure of BBD ball mill based on multi-information data fusion is researched. By combining the Rough Set and Radial Basis Function neural network, this method can not only solve the priori difficulty in obtaining information in data fusion and a large number of redundant data existing problems in system, but also greatly increase the approximation ability and learning speed of neural network. In addition, by using gradient descend algorithm with a momentum factor for RBF neural network parameters adjustment, will insure the learning speed and convergence of function. it can effectively solve the accurate material measure problem of mill in different working conditions.
DOI:10.1109/IHMSC.2009.67