DocumentCode :
420559
Title :
Dynamic system modeling based on rough sets and RBF neural networks
Author :
Zhang, Tengfei ; Xiao, Jianmei
Author_Institution :
Dept. of Electr. & Autom., Shanghai Maritime Univ., China
Volume :
1
fYear :
2004
fDate :
15-19 June 2004
Firstpage :
185
Abstract :
Rough set is a powerful mathematical tool, which can deal with fuzzy and uncertain knowledge, and radial basis function (RBF) neural network has the ability to approach any nonlinear function precisely. A dynamic modeling method is presented using the rough sets and RBF network for complex system. The method is applied to model the steam turbine generators with complex dynamic characteristics and uncertainties. The simulation results prove the validity of this method.
Keywords :
large-scale systems; modelling; nonlinear functions; radial basis function networks; rough set theory; steam turbines; turbogenerators; RBF neural networks; complex dynamic characteristics; complex dynamic uncertainties; complex system; dynamic system modeling method; nonlinear function; radial basis function; rough set theory; steam turbine generators; Character generation; Fuzzy neural networks; Fuzzy sets; Neural networks; Nonlinear dynamical systems; Power system modeling; Radial basis function networks; Rough sets; Turbines; Uncertainty;
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.2004.1340553
Filename :
1340553
Link To Document :
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