Ship synchronous generator modeling based on RST and RBF neural networks

Authors:
Xihuai Wang;Tengfei Zhang;Jianmei Xiao
Affiliations:
Department of Electrical and Automation, Shanghai Maritime University, Shanghai, China;Department of Electrical and Automation, Shanghai Maritime University, Shanghai, China;Department of Electrical and Automation, Shanghai Maritime University, Shanghai, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 2
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Abstract

Ship synchronous generator modeling is the basis of control, analysis and design in the ship power systems. According to the strong non-linear relation characteristics of ship synchronous generator, a dynamic modeling method based on rough set theory (RST) and radial basis function (RBF) neural networks is presented in this paper. With the advantage of finding useful and minimal hidden patterns in data, RST is first applied to intelligent data analysis in this algorithm, including incompatible data elimination, important input nodes selection and radial basis function centers initialization, followed by a second stage adjusting the network parameters and training the weights of hidden nodes. The experimental results proved that this method could achieve greater accuracy and generalization ability.