DocumentCode :
1573039
Title :
Modeling study of sludge process based on neural network
Author :
Yu, Ying ; Qiao, Junfei ; Ye, Xudong
Author_Institution :
Sch. of Electron. & Control Eng., Beijing Univ. of Technol., China
Volume :
4
fYear :
2004
Firstpage :
3413
Abstract :
On the basis of analyzing the classical methods of sludge process modeling, the paper put forward a new method about activated sludge process by neural networks. Firstly, the paper utilized principal component analysis method to realize reduce the dimension of the input vectors and orthogonalize the components of the input vectors. Then built activated sludge process system by BP and RBF artificial neural networks, the applicability of the two neural network models were analyzed to sludge process. The experiment result shows that: (1) these neural networks may reflect real conditions correctly and have strong self-adaptation; (2) the RBF neural network model has better convergence ability and impending speed than the BP neural network model.
Keywords :
backpropagation; principal component analysis; radial basis function networks; sludge treatment; RBF artificial neural networks; back propagation neural network; input vectors; principal component analysis; sludge process; Artificial neural networks; Control engineering; Convergence; Neural networks; Paper technology; Power supplies; Power system modeling; Principal component analysis; Sludge treatment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1343176
Filename :
1343176
Link To Document :
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